diff --git a/.coveragerc b/.coveragerc new file mode 100644 index 000000000..f4afc3ce5 --- /dev/null +++ b/.coveragerc @@ -0,0 +1,5 @@ +[report] +exclude_lines = + pragma: no cover + if\s+(typing\.)?TYPE_CHECKING: + \.\.\. diff --git a/.github/linters/.ruff.toml b/.github/linters/.ruff.toml index 10aeeccbd..03b79e181 100644 --- a/.github/linters/.ruff.toml +++ b/.github/linters/.ruff.toml @@ -61,6 +61,8 @@ ignore = [ "FBT002", # builtin-attribute-shadowing (not an issue) "A003", + # implicit-return (can add a return even though all cases are covered) + "RET503", # superfluous-else-return (sometimes it's more readable) "RET505", # superfluous-else-raise (sometimes it's more readable) diff --git a/.github/workflows/issue.yml b/.github/workflows/issue.yml index f2f826eb3..8965a210c 100644 --- a/.github/workflows/issue.yml +++ b/.github/workflows/issue.yml @@ -9,7 +9,7 @@ jobs: add-to-project: runs-on: ubuntu-latest steps: - - uses: actions/add-to-project@v0.4.1 + - uses: actions/add-to-project@v0.5.0 with: project-url: https://github.com/orgs/Safe-DS/projects/2 github-token: ${{ secrets.ADD_TO_PROJECT_PAT }} diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index c185c150c..2c43cec4b 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -17,4 +17,6 @@ jobs: working-directory: . python-version: ${{ matrix.python-version }} module-name: safeds - coverage: ${{ matrix.python-version == '3.10' }} + coverage: ${{ matrix.python-version == '3.11' }} + secrets: + CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }} diff --git a/.github/workflows/megalinter.yml b/.github/workflows/megalinter.yml index 06d8f0f73..599a7e878 100644 --- a/.github/workflows/megalinter.yml +++ b/.github/workflows/megalinter.yml @@ -7,5 +7,9 @@ on: jobs: megalinter: uses: lars-reimann/.github/.github/workflows/megalinter-reusable.yml@main + permissions: + contents: write + issues: write + pull-requests: write secrets: PAT: ${{ secrets.PAT }} diff --git a/.github/workflows/pr.yml b/.github/workflows/pr.yml index f91b1ace9..53c3174c0 100644 --- a/.github/workflows/pr.yml +++ b/.github/workflows/pr.yml @@ -21,4 +21,6 @@ jobs: working-directory: . python-version: ${{ matrix.python-version }} module-name: safeds - coverage: ${{ matrix.python-version == '3.10' }} + coverage: ${{ matrix.python-version == '3.11' }} + secrets: + CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }} diff --git a/.gitignore b/.gitignore index eab7fca54..eb88cf746 100644 --- a/.gitignore +++ b/.gitignore @@ -45,5 +45,5 @@ report/ megalinter-reports/ # Other -.DS_Store/ +.DS_Store *.log diff --git a/.mega-linter.yml b/.mega-linter.yml index f1e966080..87d022827 100644 --- a/.mega-linter.yml +++ b/.mega-linter.yml @@ -17,5 +17,4 @@ JSON_PRETTIER_FILE_EXTENSIONS: # Commands PRE_COMMANDS: - - command: npm install @lars-reimann/prettier-config - cwd: workspace + - command: npm i @lars-reimann/prettier-config diff --git a/docs/CHANGELOG.md b/docs/CHANGELOG.md index dca08252c..f9f1fdb53 100644 --- a/docs/CHANGELOG.md +++ b/docs/CHANGELOG.md @@ -1,3 +1,114 @@ +## [0.14.0](https://github.com/Safe-DS/Stdlib/compare/v0.13.0...v0.14.0) (2023-06-30) + + +### Features + +* 290 properties for width-height of image ([#359](https://github.com/Safe-DS/Stdlib/issues/359)) ([d9ebdc1](https://github.com/Safe-DS/Stdlib/commit/d9ebdc1e3deef986026abf5d90c57376dbac23ee)), closes [#290](https://github.com/Safe-DS/Stdlib/issues/290) +* Add `find_edges` method to `Image` class ([#383](https://github.com/Safe-DS/Stdlib/issues/383)) ([d14b6ce](https://github.com/Safe-DS/Stdlib/commit/d14b6ce281c8b1324f727c45ca13b99b6f9ed2a2)), closes [#288](https://github.com/Safe-DS/Stdlib/issues/288) +* Add `StandardScaler` transformer ([#316](https://github.com/Safe-DS/Stdlib/issues/316)) ([57b0572](https://github.com/Safe-DS/Stdlib/commit/57b057289ee5c862422df7d85e4fe72047567b7f)), closes [#142](https://github.com/Safe-DS/Stdlib/issues/142) +* Add docstrings to the getter methods for hyperparameters in Regression and Classification models ([#371](https://github.com/Safe-DS/Stdlib/issues/371)) ([9073f04](https://github.com/Safe-DS/Stdlib/commit/9073f04cd339fc2146ea04339ed14688f29b336f)), closes [#313](https://github.com/Safe-DS/Stdlib/issues/313) +* Added `Table.group_by` to group a table by a given key ([#343](https://github.com/Safe-DS/Stdlib/issues/343)) ([afb98be](https://github.com/Safe-DS/Stdlib/commit/afb98be2b1d0db0dad2084979537a1e5d449ba07)), closes [#160](https://github.com/Safe-DS/Stdlib/issues/160) +* Added and improved errors and warnings in the table transformers ([#372](https://github.com/Safe-DS/Stdlib/issues/372)) ([544e307](https://github.com/Safe-DS/Stdlib/commit/544e307af638f8d830171ac34e3edb61da9abc07)), closes [#152](https://github.com/Safe-DS/Stdlib/issues/152) +* added crop() method in image and tests ([#365](https://github.com/Safe-DS/Stdlib/issues/365)) ([eba8163](https://github.com/Safe-DS/Stdlib/commit/eba8163d649d1e4dcdda2ff4fc3e2229249d8940)) +* added invert_colors method ([#367](https://github.com/Safe-DS/Stdlib/issues/367)) ([1e4d110](https://github.com/Safe-DS/Stdlib/commit/1e4d110da9516fd1f95f2e9d7737ebeef250eb6a)) +* adjust brightness and contrast of image ([#368](https://github.com/Safe-DS/Stdlib/issues/368)) ([1752feb](https://github.com/Safe-DS/Stdlib/commit/1752feb06bb5b0f041310956eca2ee5ed08dd999)), closes [#289](https://github.com/Safe-DS/Stdlib/issues/289) [#291](https://github.com/Safe-DS/Stdlib/issues/291) +* blur Image method ([#363](https://github.com/Safe-DS/Stdlib/issues/363)) ([c642176](https://github.com/Safe-DS/Stdlib/commit/c6421762c7ec810137713b581f71e0b1a66176c0)) +* check that methods of table can handle an empty table ([#314](https://github.com/Safe-DS/Stdlib/issues/314)) ([686c2e7](https://github.com/Safe-DS/Stdlib/commit/686c2e7728850d9c775135b391551495f6e87451)), closes [#123](https://github.com/Safe-DS/Stdlib/issues/123) +* convert image to grayscale ([#366](https://github.com/Safe-DS/Stdlib/issues/366)) ([1312fe7](https://github.com/Safe-DS/Stdlib/commit/1312fe76dd3e79506423058b8b771788be8b54a3)), closes [#287](https://github.com/Safe-DS/Stdlib/issues/287) +* enhance `replace_column` to accept a list of new columns ([#312](https://github.com/Safe-DS/Stdlib/issues/312)) ([d50c5b5](https://github.com/Safe-DS/Stdlib/commit/d50c5b54f3c1ec70eda2fd9bfcc7148fa3e89c6b)), closes [#301](https://github.com/Safe-DS/Stdlib/issues/301) +* Explicitly throw `UnknownColumnNameError` in `TaggedTable._from_table` ([#334](https://github.com/Safe-DS/Stdlib/issues/334)) ([498999f](https://github.com/Safe-DS/Stdlib/commit/498999f50d9f48609043f87f45ec9383ace3afe1)), closes [#333](https://github.com/Safe-DS/Stdlib/issues/333) +* flip images / eq method for image ([#360](https://github.com/Safe-DS/Stdlib/issues/360)) ([54f4ae1](https://github.com/Safe-DS/Stdlib/commit/54f4ae183a0d5e6f18386cc255594f8c810c0fdf)), closes [#280](https://github.com/Safe-DS/Stdlib/issues/280) +* improve `table.summary`. Catch `ValueError` thrown by `column.stability` ([#390](https://github.com/Safe-DS/Stdlib/issues/390)) ([dbbe0e3](https://github.com/Safe-DS/Stdlib/commit/dbbe0e3a6ebd2f22c386c13a3cc53184e531f837)), closes [#320](https://github.com/Safe-DS/Stdlib/issues/320) +* improve error handling of `column.stability` when given a column that contains only None ([#388](https://github.com/Safe-DS/Stdlib/issues/388)) ([1da2499](https://github.com/Safe-DS/Stdlib/commit/1da24992d02a3cbcea12fef0b78004d99403beb0)), closes [#319](https://github.com/Safe-DS/Stdlib/issues/319) +* Improve Error Handling of classifiers and regressors ([#355](https://github.com/Safe-DS/Stdlib/issues/355)) ([66f5f64](https://github.com/Safe-DS/Stdlib/commit/66f5f647820b3bf36a6041f04d6e8547170b1c81)), closes [#153](https://github.com/Safe-DS/Stdlib/issues/153) +* Resize image ([#354](https://github.com/Safe-DS/Stdlib/issues/354)) ([3a971ca](https://github.com/Safe-DS/Stdlib/commit/3a971ca3c88b86d7a780d3f163a2ee86ab35b98c)), closes [#283](https://github.com/Safe-DS/Stdlib/issues/283) +* rotate_left and rotate_right added to Image ([#361](https://github.com/Safe-DS/Stdlib/issues/361)) ([c877530](https://github.com/Safe-DS/Stdlib/commit/c8775306deffe826cb14d0fe177e0c09aa4204fd)), closes [#281](https://github.com/Safe-DS/Stdlib/issues/281) +* set kernel of support vector machine ([#350](https://github.com/Safe-DS/Stdlib/issues/350)) ([1326f40](https://github.com/Safe-DS/Stdlib/commit/1326f40a260437a373711c7c65a986bcfa321980)), closes [#172](https://github.com/Safe-DS/Stdlib/issues/172) +* sharpen image ([#364](https://github.com/Safe-DS/Stdlib/issues/364)) ([3444700](https://github.com/Safe-DS/Stdlib/commit/344470004199dcf6763b1145ce38fdf14e11483a)), closes [#286](https://github.com/Safe-DS/Stdlib/issues/286) + + +### Bug Fixes + +* Keeping no columns with Table.keep_only_columns results in an empty Table with a row count above 0 ([#386](https://github.com/Safe-DS/Stdlib/issues/386)) ([15dab06](https://github.com/Safe-DS/Stdlib/commit/15dab06d87a8f8cff7e676b67145eff55824134a)), closes [#318](https://github.com/Safe-DS/Stdlib/issues/318) +* remove default value of `positive_class` parameter of classifier metrics ([#382](https://github.com/Safe-DS/Stdlib/issues/382)) ([58fc09e](https://github.com/Safe-DS/Stdlib/commit/58fc09eab2db1fb678522a807a8a20ed519627fd)) +* remove default value of `radius` parameter of `blur` ([#378](https://github.com/Safe-DS/Stdlib/issues/378)) ([7f07f29](https://github.com/Safe-DS/Stdlib/commit/7f07f29ad0b426861b929297305e61e0c2d93ebc)) + +## [0.13.0](https://github.com/Safe-DS/Stdlib/compare/v0.12.0...v0.13.0) (2023-06-01) + + +### Features + +* add `Choice` class for possible values of hyperparameter ([#325](https://github.com/Safe-DS/Stdlib/issues/325)) ([d511c3e](https://github.com/Safe-DS/Stdlib/commit/d511c3eed779e64fc53499e7c2eb2e8292955645)), closes [#264](https://github.com/Safe-DS/Stdlib/issues/264) +* Add `RangeScaler` transformer ([#310](https://github.com/Safe-DS/Stdlib/issues/310)) ([f687840](https://github.com/Safe-DS/Stdlib/commit/f68784057afe20d5450e9eb875fce1a07fb5fa77)), closes [#141](https://github.com/Safe-DS/Stdlib/issues/141) +* Add methods that tell which columns would be affected by a transformer ([#304](https://github.com/Safe-DS/Stdlib/issues/304)) ([3933b45](https://github.com/Safe-DS/Stdlib/commit/3933b458042f524d337f41d0ffa3aa4da16f5a2e)), closes [#190](https://github.com/Safe-DS/Stdlib/issues/190) +* Getters for hyperparameters of Regression and Classification models ([#306](https://github.com/Safe-DS/Stdlib/issues/306)) ([5c7a662](https://github.com/Safe-DS/Stdlib/commit/5c7a6623cd47f7c6cc25d2cd02179ff5b1a520d9)), closes [#260](https://github.com/Safe-DS/Stdlib/issues/260) +* improve error handling of table ([#308](https://github.com/Safe-DS/Stdlib/issues/308)) ([ef87cc4](https://github.com/Safe-DS/Stdlib/commit/ef87cc4d7f62fd0830688f8535e53ad7e2329457)), closes [#147](https://github.com/Safe-DS/Stdlib/issues/147) +* Remove warnings thrown in new `Transformer` methods ([#324](https://github.com/Safe-DS/Stdlib/issues/324)) ([ca046c4](https://github.com/Safe-DS/Stdlib/commit/ca046c40217bebcc05af98129d4e194c0509c9bb)), closes [#323](https://github.com/Safe-DS/Stdlib/issues/323) + +## [0.12.0](https://github.com/Safe-DS/Stdlib/compare/v0.11.0...v0.12.0) (2023-05-11) + + +### Features + +* add `learning_rate` to AdaBoost classifier and regressor. ([#251](https://github.com/Safe-DS/Stdlib/issues/251)) ([7f74440](https://github.com/Safe-DS/Stdlib/commit/7f744409c08fb465d59f1f04e2cac7ebed23f339)), closes [#167](https://github.com/Safe-DS/Stdlib/issues/167) +* add alpha parameter to `lasso_regression` ([#232](https://github.com/Safe-DS/Stdlib/issues/232)) ([b5050b9](https://github.com/Safe-DS/Stdlib/commit/b5050b91f17774fa5cf3fc80b51d3ea6c295481f)), closes [#163](https://github.com/Safe-DS/Stdlib/issues/163) +* add parameter `lasso_ratio` to `ElasticNetRegression` ([#237](https://github.com/Safe-DS/Stdlib/issues/237)) ([4a1a736](https://github.com/Safe-DS/Stdlib/commit/4a1a7367099125d2a072bf36686063de7180e8f0)), closes [#166](https://github.com/Safe-DS/Stdlib/issues/166) +* Add parameter `number_of_tree` to `RandomForest` classifier and regressor ([#230](https://github.com/Safe-DS/Stdlib/issues/230)) ([414336a](https://github.com/Safe-DS/Stdlib/commit/414336ac9752f961cab30545cbe51befbde50d21)), closes [#161](https://github.com/Safe-DS/Stdlib/issues/161) +* Added `Table.plot_boxplots` to plot a boxplot for each numerical column in the table ([#254](https://github.com/Safe-DS/Stdlib/issues/254)) ([0203a0c](https://github.com/Safe-DS/Stdlib/commit/0203a0c977184cdee1769d317fcb1f7cb5c644f3)), closes [#156](https://github.com/Safe-DS/Stdlib/issues/156) [#239](https://github.com/Safe-DS/Stdlib/issues/239) +* Added `Table.plot_histograms` to plot a histogram for each column in the table ([#252](https://github.com/Safe-DS/Stdlib/issues/252)) ([e27d410](https://github.com/Safe-DS/Stdlib/commit/e27d410085ebaf9ab98069a5b175d800259d95a3)), closes [#157](https://github.com/Safe-DS/Stdlib/issues/157) +* Added `Table.transform_table` method which returns the transformed Table ([#229](https://github.com/Safe-DS/Stdlib/issues/229)) ([0a9ce72](https://github.com/Safe-DS/Stdlib/commit/0a9ce72ba2101f99fea43dcd43b1f498dbb8e558)), closes [#110](https://github.com/Safe-DS/Stdlib/issues/110) +* Added alpha parameter to `RidgeRegression` ([#231](https://github.com/Safe-DS/Stdlib/issues/231)) ([1ddc948](https://github.com/Safe-DS/Stdlib/commit/1ddc948aac5f153f649c3869b99184c2c1d96d9f)), closes [#164](https://github.com/Safe-DS/Stdlib/issues/164) +* Added Column#transform ([#270](https://github.com/Safe-DS/Stdlib/issues/270)) ([40fb756](https://github.com/Safe-DS/Stdlib/commit/40fb7566307b4c015f3acae7bb94f4e937977e07)), closes [#255](https://github.com/Safe-DS/Stdlib/issues/255) +* Added method `Table.inverse_transform_table` which returns the original table ([#227](https://github.com/Safe-DS/Stdlib/issues/227)) ([846bf23](https://github.com/Safe-DS/Stdlib/commit/846bf233235b2cdaf9bbd00cacb89ea44e94011b)), closes [#111](https://github.com/Safe-DS/Stdlib/issues/111) +* Added parameter `c` to `SupportVectorMachines` ([#267](https://github.com/Safe-DS/Stdlib/issues/267)) ([a88eb8b](https://github.com/Safe-DS/Stdlib/commit/a88eb8b8c3f67e8485ce2847c4923a2cf0506f68)), closes [#169](https://github.com/Safe-DS/Stdlib/issues/169) +* Added parameter `maximum_number_of_learner` and `learner` to `AdaBoost` ([#269](https://github.com/Safe-DS/Stdlib/issues/269)) ([bb5a07e](https://github.com/Safe-DS/Stdlib/commit/bb5a07e17b6563d394d79b62349633791675346f)), closes [#171](https://github.com/Safe-DS/Stdlib/issues/171) [#173](https://github.com/Safe-DS/Stdlib/issues/173) +* Added parameter `number_of_trees` to `GradientBoosting` ([#268](https://github.com/Safe-DS/Stdlib/issues/268)) ([766f2ff](https://github.com/Safe-DS/Stdlib/commit/766f2ff2a6d68098be3e858ad12bf9e509e5f192)), closes [#170](https://github.com/Safe-DS/Stdlib/issues/170) +* Allow arguments of type pathlib.Path for file I/O methods ([#228](https://github.com/Safe-DS/Stdlib/issues/228)) ([2b58c82](https://github.com/Safe-DS/Stdlib/commit/2b58c82f50ce88b4778f3c82108f5d5f474fdfa9)), closes [#146](https://github.com/Safe-DS/Stdlib/issues/146) +* convert `Schema` to `dict` and format it nicely in a notebook ([#244](https://github.com/Safe-DS/Stdlib/issues/244)) ([ad1cac5](https://github.com/Safe-DS/Stdlib/commit/ad1cac5198709d0a78019787251ba2aed913cf55)), closes [#151](https://github.com/Safe-DS/Stdlib/issues/151) +* Convert between Excel file and `Table` ([#233](https://github.com/Safe-DS/Stdlib/issues/233)) ([0d7a998](https://github.com/Safe-DS/Stdlib/commit/0d7a998f9e660f47147f7eaa6ebb8119c09188ac)), closes [#138](https://github.com/Safe-DS/Stdlib/issues/138) [#139](https://github.com/Safe-DS/Stdlib/issues/139) +* convert containers for tabular data to HTML ([#243](https://github.com/Safe-DS/Stdlib/issues/243)) ([683c279](https://github.com/Safe-DS/Stdlib/commit/683c2793f053f5d0572e723b35db383aa00ddc44)), closes [#140](https://github.com/Safe-DS/Stdlib/issues/140) +* make `Column` a subclass of `Sequence` ([#245](https://github.com/Safe-DS/Stdlib/issues/245)) ([a35b943](https://github.com/Safe-DS/Stdlib/commit/a35b943a126b28500499f5d7da1bccee10d98ff3)) +* mark optional hyperparameters as keyword only ([#296](https://github.com/Safe-DS/Stdlib/issues/296)) ([44a41eb](https://github.com/Safe-DS/Stdlib/commit/44a41eb205ad0f69f01564ab318e53873bb902c4)), closes [#278](https://github.com/Safe-DS/Stdlib/issues/278) +* move exceptions back to common package ([#295](https://github.com/Safe-DS/Stdlib/issues/295)) ([a91172c](https://github.com/Safe-DS/Stdlib/commit/a91172c0f21ea9934cedbe9fd749eb4ff7929394)), closes [#177](https://github.com/Safe-DS/Stdlib/issues/177) [#262](https://github.com/Safe-DS/Stdlib/issues/262) +* precision metric for classification ([#272](https://github.com/Safe-DS/Stdlib/issues/272)) ([5adadad](https://github.com/Safe-DS/Stdlib/commit/5adadadf6ab185b4d8864b7859d7cc036a055a6d)), closes [#185](https://github.com/Safe-DS/Stdlib/issues/185) +* Raise error if an untagged table is used instead of a `TaggedTable` ([#234](https://github.com/Safe-DS/Stdlib/issues/234)) ([8eea3dd](https://github.com/Safe-DS/Stdlib/commit/8eea3dd31dab49b4d9371f61f02ace9fdca25394)), closes [#192](https://github.com/Safe-DS/Stdlib/issues/192) +* recall and F1-score metrics for classification ([#277](https://github.com/Safe-DS/Stdlib/issues/277)) ([2cf93cc](https://github.com/Safe-DS/Stdlib/commit/2cf93cc7181ad69991055dd0e49035a785105356)), closes [#187](https://github.com/Safe-DS/Stdlib/issues/187) [#186](https://github.com/Safe-DS/Stdlib/issues/186) +* replace prefix `n` with `number_of` ([#250](https://github.com/Safe-DS/Stdlib/issues/250)) ([f4f44a6](https://github.com/Safe-DS/Stdlib/commit/f4f44a6b8d5f8ee795673b11c5f00e3ebb1b1b39)), closes [#171](https://github.com/Safe-DS/Stdlib/issues/171) +* set `alpha` parameter for regularization of `ElasticNetRegression` ([#238](https://github.com/Safe-DS/Stdlib/issues/238)) ([e642d1d](https://github.com/Safe-DS/Stdlib/commit/e642d1d49c5b21240fa5bbbde48e80d5b7743ff1)), closes [#165](https://github.com/Safe-DS/Stdlib/issues/165) +* Set `column_names` in `fit` methods of table transformers to be required ([#225](https://github.com/Safe-DS/Stdlib/issues/225)) ([2856296](https://github.com/Safe-DS/Stdlib/commit/2856296fb7228e8d4adebceb86e22ecaeb609ad9)), closes [#179](https://github.com/Safe-DS/Stdlib/issues/179) +* set learning rate of Gradient Boosting models ([#253](https://github.com/Safe-DS/Stdlib/issues/253)) ([9ffaf55](https://github.com/Safe-DS/Stdlib/commit/9ffaf55a97333bb2edce2f2c9c66650a9724ca60)), closes [#168](https://github.com/Safe-DS/Stdlib/issues/168) +* Support vector machine for regression and for classification ([#236](https://github.com/Safe-DS/Stdlib/issues/236)) ([7f6c3bd](https://github.com/Safe-DS/Stdlib/commit/7f6c3bd9fba670a487d3ef96d281f3904a8974a7)), closes [#154](https://github.com/Safe-DS/Stdlib/issues/154) +* usable constructor for `Table` ([#294](https://github.com/Safe-DS/Stdlib/issues/294)) ([56a1fc4](https://github.com/Safe-DS/Stdlib/commit/56a1fc4450ba77877b6b29467c0e1d11dd200f9d)), closes [#266](https://github.com/Safe-DS/Stdlib/issues/266) +* usable constructor for `TaggedTable` ([#299](https://github.com/Safe-DS/Stdlib/issues/299)) ([01c3ad9](https://github.com/Safe-DS/Stdlib/commit/01c3ad9564a35f31744a30862ae1a533ef5adf6b)), closes [#293](https://github.com/Safe-DS/Stdlib/issues/293) + + +### Bug Fixes + +* OneHotEncoder no longer creates duplicate column names ([#271](https://github.com/Safe-DS/Stdlib/issues/271)) ([f604666](https://github.com/Safe-DS/Stdlib/commit/f604666305d38d3a01696ea7ca60056ce7f78245)), closes [#201](https://github.com/Safe-DS/Stdlib/issues/201) +* selectively ignore one warning instead of all warnings ([#235](https://github.com/Safe-DS/Stdlib/issues/235)) ([3aad07d](https://github.com/Safe-DS/Stdlib/commit/3aad07ddcc0da42e1dab2eed49fc41433a876765)) + +## [0.11.0](https://github.com/Safe-DS/Stdlib/compare/v0.10.0...v0.11.0) (2023-04-21) + + +### Features + +* `OneHotEncoder.inverse_transform` now maintains the column order from the original table ([#195](https://github.com/Safe-DS/Stdlib/issues/195)) ([3ec0041](https://github.com/Safe-DS/Stdlib/commit/3ec0041669ffe97640f96db345f3f43720d5c3f7)), closes [#109](https://github.com/Safe-DS/Stdlib/issues/109) +* add `plot_` prefix back to plotting methods ([#212](https://github.com/Safe-DS/Stdlib/issues/212)) ([e50c3b0](https://github.com/Safe-DS/Stdlib/commit/e50c3b0118825e33eef0e2a7073673603e316ee5)), closes [#211](https://github.com/Safe-DS/Stdlib/issues/211) +* adjust `Column`, `Schema` and `Table` to changes in `Row` ([#216](https://github.com/Safe-DS/Stdlib/issues/216)) ([ca3eebb](https://github.com/Safe-DS/Stdlib/commit/ca3eebbe2166f08d76cdcb89a012518ae8ff8e4e)) +* back `Row` by a `polars.DataFrame` ([#214](https://github.com/Safe-DS/Stdlib/issues/214)) ([62ca34d](https://github.com/Safe-DS/Stdlib/commit/62ca34dd399da8b4e34b89bad408311707b53f41)), closes [#196](https://github.com/Safe-DS/Stdlib/issues/196) [#149](https://github.com/Safe-DS/Stdlib/issues/149) +* clean up `Row` class ([#215](https://github.com/Safe-DS/Stdlib/issues/215)) ([b12fc68](https://github.com/Safe-DS/Stdlib/commit/b12fc68c9b914446c1ea3aca5dacfab969680ae8)) +* convert between `Row` and `dict` ([#206](https://github.com/Safe-DS/Stdlib/issues/206)) ([e98b653](https://github.com/Safe-DS/Stdlib/commit/e98b6536a2c50e64772fc7aeb29c03c850ebd570)), closes [#204](https://github.com/Safe-DS/Stdlib/issues/204) +* convert between a `dict` and a `Table` ([#198](https://github.com/Safe-DS/Stdlib/issues/198)) ([2a5089e](https://github.com/Safe-DS/Stdlib/commit/2a5089e408a1eeb078aa77ce7c3e5ae8c4bc0201)), closes [#197](https://github.com/Safe-DS/Stdlib/issues/197) +* create column types for `polars` data types ([#208](https://github.com/Safe-DS/Stdlib/issues/208)) ([e18b362](https://github.com/Safe-DS/Stdlib/commit/e18b36250ac170e3364106ba1c59649e0b4aff21)), closes [#196](https://github.com/Safe-DS/Stdlib/issues/196) +* dataframe interchange protocol ([#200](https://github.com/Safe-DS/Stdlib/issues/200)) ([bea976a](https://github.com/Safe-DS/Stdlib/commit/bea976a72a28698a29145a3ad501d8af59e7e5d8)), closes [#199](https://github.com/Safe-DS/Stdlib/issues/199) +* move existing ML solutions into `safeds.ml.classical` package ([#213](https://github.com/Safe-DS/Stdlib/issues/213)) ([655f07f](https://github.com/Safe-DS/Stdlib/commit/655f07f7f8f02b8fc92b469daf15a2384a81534f)), closes [#210](https://github.com/Safe-DS/Stdlib/issues/210) + + +### Bug Fixes + +* `table.keep_only_columns` now maps column names to correct data ([#194](https://github.com/Safe-DS/Stdlib/issues/194)) ([459ab75](https://github.com/Safe-DS/Stdlib/commit/459ab7570c7c7b79304f78cab4f6bff82fc026a3)), closes [#115](https://github.com/Safe-DS/Stdlib/issues/115) +* typo in type hint ([#184](https://github.com/Safe-DS/Stdlib/issues/184)) ([e79727d](https://github.com/Safe-DS/Stdlib/commit/e79727d5d91090bc5cd6d3a81acc2a1393b3e5eb)), closes [#180](https://github.com/Safe-DS/Stdlib/issues/180) + ## [0.10.0](https://github.com/Safe-DS/Stdlib/compare/v0.9.0...v0.10.0) (2023-04-13) diff --git a/docs/README.md b/docs/README.md index b9366e6f9..0afaccd23 100644 --- a/docs/README.md +++ b/docs/README.md @@ -3,7 +3,7 @@ [![PyPI](https://img.shields.io/pypi/v/safe-ds)](https://pypi.org/project/safe-ds) [![Main](https://github.com/Safe-DS/Stdlib/actions/workflows/main.yml/badge.svg)](https://github.com/Safe-DS/Stdlib/actions/workflows/main.yml) [![codecov](https://codecov.io/gh/Safe-DS/Stdlib/branch/main/graph/badge.svg?token=HVRP1633B1)](https://codecov.io/gh/Safe-DS/Stdlib) -[![Documentation Status](https://readthedocs.org/projects/safe-ds-stdlib/badge/?version=stable)](https://stdlib.safe-ds.com) +[![Documentation Status](https://readthedocs.org/projects/safe-ds-stdlib/badge/?version=stable)](https://stdlib.safeds.com) A user-friendly library for Data Science (DS) in Python. @@ -17,11 +17,7 @@ Instead of implementing DS methods from scratch, we use established DS libraries For more specialized tasks, we recommend using these or other DS libraries directly. -Note that this library is still in development and not yet ready for production. Expect breaking changes in the future without a major version bump (while in the `0.y.z` version range). Feedback is very welcome! If you have a suggestion or find a bug, please [open an issue](https://github.com/Safe-DS/Stdlib/issues/new/choose). If you have a question, please [use our discussion forum](https://github.com/orgs/Safe-DS/discussions). - -## Documentation - -You can find the full documentation [here](https://stdlib.safe-ds.com). +Note that this library is still in development and not yet ready for production. Expect breaking changes in the future without a major version bump (while in the `0.y.z` version range). Feedback is very welcome, however! If you have a suggestion or find a bug, please [open an issue](https://github.com/Safe-DS/Stdlib/issues/new/choose). If you have a question, please [use our discussion forum][forum]. ## Installation @@ -30,3 +26,19 @@ Get the latest version from [PyPI](https://pypi.org/project/safe-ds): ```shell pip install safe-ds ``` + +## Documentation + +You can find the full documentation [here](https://stdlib.safeds.com). + +## Contributing + +We welcome contributions from everyone. As a starting point, check the following resources: + +* [Setting up a development environment](https://stdlib.safeds.com/en/latest/development/environment/) +* [Project guidelines](https://stdlib.safeds.com/en/latest/development/project_guidelines/) +* [Contributing page](https://github.com/Safe-DS/Stdlib/contribute) + +If you need further help, please [use our discussion forum][forum]. + +[forum]: https://github.com/orgs/Safe-DS/discussions diff --git a/docs/development/code_review.md b/docs/development/code_review.md new file mode 100644 index 000000000..075ac0f27 --- /dev/null +++ b/docs/development/code_review.md @@ -0,0 +1,72 @@ +# Code review + +This document describes +general guidelines for developers +when reviewing a pull request (PR). + +## Verify that the PR solves the addressed issue + +### Understand the issue + +* Read the issue that was addressed by the PR and make sure you understand it +* Read any discussions that occurred on the issue page + +### Check the PR + +* Check the PR for __completeness__: Was every point from the issue covered? +* Check for __correctness__: Were the problems described in the issue solved in the intended way? +* (For PRs that introduce new features:) Check the design - does it comply with our [project guidelines][guidelines-general]? +* Check for potential bugs - edge cases, exceptions that may be raised in functions that were called etc. +* Check the code style: Is it readable? Do variables have sensible names? Is it consistent to existing code? Is there a better / more elegant way to do certain things (e.g. [f-string](https://docs.python.org/3/tutorial/inputoutput.html#tut-f-strings) instead of manual string concatenation)? +* Check any warnings reported by your IDE + +## Check the tests + +* Run the tests locally +* Check if there are any warnings that are not caught by the test suite +* Make sure the test cases are complete - do they cover all edge cases (e.g. empty tables)? +* Make sure they comply to our [project guidelines][guidelines-tests] - are the tests parametrized and do all testcases have descriptive IDs? + +## Verify that the PR does not break existing code + +This is largely covered by the automated tests. +However, you should always: + +* Make sure all tests actually ran through (pytest, linter, code coverage) +* Make sure that the branch is up-to-date with the `main` branch, so you actually test the behaviour that will result once the feature branch is merged + +## Check the PR format + +* Check that the PR title starts with a fitting [type](https://github.com/Safe-DS/.github/blob/main/.github/CONTRIBUTING.md#types) (e.g. `feat`, `fix`, `docs`, ...) +* Check that the changes introduced by the PR are documented in the PR message + +## Requesting changes + +If you found any issues with the reviewed PR, +navigate to the `Files changed` tab in the PR page +and click on the respective line +to add your comments. + +For more details on specific review features, +check out the [documentation on GitHub][github-review]. + +## Finishing the review + +When done, finish your review +by navigating to the `Files changed` tab +and clicking the green `Review changes` button +on the top right. + +If you found no issues, +select the `Approve` option +to approve the changes made by the PR. +If you like, you can add a "LGTM" meme +from [lgtm.party](https://lgtm.party/), +preferably one with a cat image. + +If you found any problems, +select `Request Changes` instead. + +[guidelines-general]: project_guidelines.md +[guidelines-tests]: project_guidelines.md#tests +[github-review]: https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/reviewing-changes-in-pull-requests diff --git a/docs/development/guidelines.md b/docs/development/guidelines.md deleted file mode 100644 index 19664bf1e..000000000 --- a/docs/development/guidelines.md +++ /dev/null @@ -1,204 +0,0 @@ -# Guidelines - -This document describes general guidelines for the Safe-DS Python Library. In the **DO**/**DON'T** examples below we either show _client code_ to describe the code users should/shouldn't have to write, or _library code_ to describe the code we, as library developers, need to write to achieve readable client code. We'll continuously update this document as we find new categories of usability issues. - -## Prefer a usable API to simple implementation - -It's more important to provide a user-friendly API to many people than to save some of our time when implementing the functionality. - -## Document API elements - -All classes should have - -* a short description, -* examples that show how to use them correctly, -* a description of their attributes. - -All functions should have - -* a short description, -* examples that show how to use them correctly, -* a description of their parameters, -* a description of their results, -* a description of any exceptions that are raised. - -The documentation should follow the [numpydoc](https://numpydoc.readthedocs.io/en/latest/format.html) format. - -## Test non-trivial functions - -If a function contains more code than just the getting or setting of a value, automated test should be added to the [`tests`][tests-folder] folder. The file structure in the tests folder should mirror the file structure of the [`src`][src-folder] folder. - -## Prefer named functions to overloaded operators - -The names can better convey the intention of the programmer and enable better auto-completion. - -!!! success "**DO** (client code):" - - ```py - table.keep_columns("name", "age") - ``` - -!!! failure "**DON'T** (client code):" - - ```py - table[["name", "age"]] - ``` - -## Prefer methods to global functions - -This aids discoverability and again enables better auto-completion. It also supports polymorphism. - -!!! success "**DO** (client code):" - - ```py - model.fit(training_data) - ``` - -!!! failure "**DON'T** (client code):" - - ```py - fit(model, training_data) - ``` - -## Prefer separate functions to functions with a flag parameter - -Some flag parameters drastically alter the semantics of a function. This can lead to confusion, and, if the parameter is optional, to errors if the default value is kept unknowingly. In such cases having two separate functions is preferable. - -!!! success "**DO** (client code):" - - ```py - table.drop_columns("name") - ``` - -!!! failure "**DON'T** (client code):" - - ```py - table.drop("name", axis="columns") - ``` - -## Avoid uncommon abbreviations - -Write full words rather than abbreviations. The increased verbosity is offset by better readability, better functioning auto-completion, and a reduced need to consult the documentation when writing code. Common abbreviations like CSV or HTML are fine though, since they rarely require explanation. - -!!! success "**DO** (client code):" - - ```py - figure.set_color_scheme(ColorScheme.AUTUMN) - ``` - -!!! failure "**DON'T** (client code):" - - ```py - figure.scs(CS.AUT) - ``` - -## Specify types of parameters and results - -Use [type hints](https://docs.python.org/3/library/typing.html) to describe the types of parameters and results of functions. This enables static type checking of client code. - -!!! success "**DO** (library code):" - - ```py - def add_ints(a: int, b: int) -> int: - return a + b - ``` - -!!! failure "**DON'T** (library code):" - - ```py - def add_ints(a, b): - return a + b - ``` - -## Use narrow data types - -Use data types that can accurately model the legal values of a declaration. This improves static detection of wrong client code. - -!!! success "**DO** (client code):" - - ```py - SupportVectorMachine(kernel=Kernel.LINEAR) # (enum) - ``` - -!!! failure "**DON'T** (client code):" - - ```py - SupportVectorMachine(kernel="linear") # (string) - ``` - -## Check preconditions of functions and fail early - -Not all preconditions of functions can be described with type hints but must instead be checked at runtime. This should be done as early as possible, usually right at the top of the body of a function. If the preconditions fail, execution of the function should halt and either a sensible value be returned (if possible) or an exception with a descriptive message be raised. - -!!! success "**DO** (library code):" - - ```py - def nth_prime(n: int) -> int: - if n <= 0: - raise ValueError(f"n must be at least 1 but was {n}.") - - # compute nth prime - ``` - -!!! failure "**DON'T** (library code):" - - ```py - def nth_prime(n: int) -> int: - # compute nth prime - ``` - -## Raise either Python exceptions or custom exceptions - -The user should not have to deal with exceptions that are defined in the wrapper libraries. So, any exceptions that may be raised when a third-party function is called should be caught and a core Python exception or a custom exception should be raised instead. The exception to this rule is when we call a callable created by the user: In this case, we just pass any exceptions thrown by this callable along. - -!!! success "**DO** (library code):" - - ```py - def read_csv(path: str) -> Table: - try: - return pd.read_csv(path) # May raise a pd.ParserError - except pd.ParserError as e: - raise FileFormatException("The loaded file is not a CSV file.") from e - ``` - -!!! failure "**DON'T** (library code):" - - ```py - def read_csv(path: str) -> Table: - return pd.read_csv(path) # May raise a pd.ParserError - ``` - -## Group API elements by task - -Packages should correspond to a specific task like classification or imputation. This eases discovery and makes it easy to switch between different solutions for the same task. - -!!! success "**DO** (client code):" - - ```py - from sklearn.classification import SupportVectorMachine - ``` - -!!! failure "**DON'T** (client code):" - - ```py - from sklearn.svm import SupportVectorMachine - ``` - -## Group values that are used together into an object - -Passing values that are commonly used together around separately is tedious, verbose, and error prone. - -!!! success "**DO** (client code):" - - ```py - training_data, validation_data = split(full_data) - ``` - -!!! failure "**DON'T** (client code):" - - ```py - training_feature_vectors, validation_feature_vectors, training_target_values, validation_target_values = split(feature_vectors, target_values) - ``` - -[src-folder]: https://github.com/Safe-DS/Stdlib/tree/main/src -[tests-folder]: https://github.com/Safe-DS/Stdlib/tree/main/tests diff --git a/docs/development/project_guidelines.md b/docs/development/project_guidelines.md new file mode 100644 index 000000000..22cfcbb87 --- /dev/null +++ b/docs/development/project_guidelines.md @@ -0,0 +1,507 @@ +# Project Guidelines + +This document describes general guidelines for the Safe-DS Python Library. In the **DO**/**DON'T** examples below we either show _client code_ to describe the code users should/shouldn't have to write, or _library code_ to describe the code we, as library developers, need to write to achieve readable client code. We'll continuously update this document as we find new categories of usability issues. + +## Design + +### Prefer a usable API to simple implementation + +It's more important to provide a user-friendly API to many people than to save some of our time when implementing the functionality. + +### Prefer named functions to overloaded operators + +The names can better convey the intention of the programmer and enable better auto-completion. + +!!! success "**DO** (client code):" + + ```py + table.keep_columns("name", "age") + ``` + +!!! failure "**DON'T** (client code):" + + ```py + table[["name", "age"]] + ``` + +### Prefer methods to global functions + +This aids discoverability and again enables better auto-completion. It also supports polymorphism. + +!!! success "**DO** (client code):" + + ```py + model.fit(training_data) + ``` + +!!! failure "**DON'T** (client code):" + + ```py + fit(model, training_data) + ``` + +### Prefer separate functions to functions with a flag parameter + +Some flag parameters drastically alter the semantics of a function. This can lead to confusion, and, if the parameter is optional, to errors if the default value is kept unknowingly. In such cases having two separate functions is preferable. + +!!! success "**DO** (client code):" + + ```py + table.drop_columns("name") + ``` + +!!! failure "**DON'T** (client code):" + + ```py + table.drop("name", axis="columns") + ``` + +### Return copies of objects + +Modifying objects in-place +can lead to surprising behaviour +and hard-to-find bugs. +Methods shall never change +the object they're called on +or any of their parameters. + +!!! success "**DO** (library code):" + + ```py + result = self._data.copy() + result.columns = self._schema.column_names + result[new_column.name] = new_column._data + return Table._from_pandas_dataframe(result) + ``` + +!!! failure "**DON'T** (library code):" + + ```py + self._data.add(new_column, axis='column') + ``` + +The corresponding docstring should explicitly state +that a method returns a copy: + +!!! success "**DO** (library code):" + + ```py + """ + Return a new table with the given column added as the last column. + The original table is not modified. + ... + """ + ``` + +### Avoid uncommon abbreviations + +Write full words rather than abbreviations. The increased verbosity is offset by better readability, better functioning auto-completion, and a reduced need to consult the documentation when writing code. Common abbreviations like CSV or HTML are fine though, since they rarely require explanation. + +!!! success "**DO** (client code):" + + ```py + figure.set_color_scheme(ColorScheme.AUTUMN) + ``` + +!!! failure "**DON'T** (client code):" + + ```py + figure.scs(CS.AUT) + ``` + +### Place more important parameters first + +Parameters that are more important to the user should be placed first. This also applies to [keyword-only parameters](#consider-marking-optional-parameters-as-keyword-only), since they still have a fixed order in the documentation. In particular, parameters of model constructors should have the following order: + +1. Model hyperparameters (e.g., the number of trees in a random forest) +2. Algorithm hyperparameters (e.g., the learning rate of a gradient boosting algorithm) +3. Regularization hyperparameters (e.g., the maximum depth of a decision tree) +4. Other parameters (e.g., the random seed) + +!!! success "**DO** (library code):" + + ```py + class GradientBoosting: + def __init__( + self, + *, + number_of_trees: int = 100, + learning_rate: float = 0.1, + random_seed: int = 0, + ) -> None: + ... + ``` + +!!! failure "**DON'T** (library code):" + + ```py + class GradientBoosting: + def __init__( + self, + *, + random_seed: int = 0, + number_of_trees: int = 100, + learning_rate: float = 0.1, + ) -> None: + ... + ``` + +### Consider marking optional parameters as keyword-only + +_Keyword-only parameters_ are parameters that can only be passed by name. It prevents users from accidentally passing a value to the wrong parameter. This can happen easily if several parameters have the same type. Moreover, marking a parameter as keyword-only allows us to change the order of parameters without breaking client code. Because of this, strongly consider marking optional parameters as keyword-only. In particular, optional hyperparameters of models should be keyword-only. + +!!! success "**DO** (library code):" + + ```py + class RandomForest: + def __init__(self, *, number_of_trees: int = 100) -> None: + ... + ``` + +!!! failure "**DON'T** (library code):" + + ```py + class RandomForest: + def __init__(self, number_of_trees: int = 100) -> None: + ... + ``` + +### Specify types of parameters and results + +Use [type hints](https://docs.python.org/3/library/typing.html) to describe the types of parameters and results of functions. This enables static type checking of client code. + +!!! success "**DO** (library code):" + + ```py + def add_ints(a: int, b: int) -> int: + return a + b + ``` + +!!! failure "**DON'T** (library code):" + + ```py + def add_ints(a, b): + return a + b + ``` + +### Use narrow data types + +Use data types that can accurately model the legal values of a declaration. This improves static detection of wrong client code. + +!!! success "**DO** (client code):" + + ```py + SupportVectorMachine(kernel=Kernel.LINEAR) # (enum) + ``` + +!!! failure "**DON'T** (client code):" + + ```py + SupportVectorMachine(kernel="linear") # (string) + ``` + +### Check preconditions of functions and fail early + +Not all preconditions of functions can be described with type hints but must instead be checked at runtime. This should be done as early as possible, usually right at the top of the body of a function. If the preconditions fail, execution of the function should halt and either a sensible value be returned (if possible) or an exception with a descriptive message be raised. + +!!! success "**DO** (library code):" + + ```py + def nth_prime(n: int) -> int: + if n <= 0: + raise ValueError(f"n must be at least 1 but was {n}.") + + # compute nth prime + ``` + +!!! failure "**DON'T** (library code):" + + ```py + def nth_prime(n: int) -> int: + # compute nth prime + ``` + +### Raise either Python exceptions or custom exceptions + +The user should not have to deal with exceptions that are defined in the wrapper libraries. So, any exceptions that may be raised when a third-party function is called should be caught and a core Python exception or a custom exception should be raised instead. The exception to this rule is when we call a callable created by the user: In this case, we just pass any exceptions thrown by this callable along. + +!!! success "**DO** (library code):" + + ```py + def read_csv(path: str) -> Table: + try: + return pd.read_csv(path) # May raise a pd.ParserError + except pd.ParserError as e: + raise FileFormatException("The loaded file is not a CSV file.") from e + ``` + +!!! failure "**DON'T** (library code):" + + ```py + def read_csv(path: str) -> Table: + return pd.read_csv(path) # May raise a pd.ParserError + ``` + +### Group API elements by task + +Packages should correspond to a specific task like classification or imputation. This eases discovery and makes it easy to switch between different solutions for the same task. + +!!! success "**DO** (client code):" + + ```py + from sklearn.classification import SupportVectorMachine + ``` + +!!! failure "**DON'T** (client code):" + + ```py + from sklearn.svm import SupportVectorMachine + ``` + +### Group values that are used together into an object + +Passing values that are commonly used together around separately is tedious, verbose, and error prone. + +!!! success "**DO** (client code):" + + ```py + training_data, validation_data = split(full_data) + ``` + +!!! failure "**DON'T** (client code):" + + ```py + training_feature_vectors, validation_feature_vectors, training_target_values, validation_target_values = split(feature_vectors, target_values) + ``` + +## Docstrings + +The docstrings **should** use the [numpydoc](https://numpydoc.readthedocs.io/en/latest/format.html) format. The descriptions **should not** start with "this" and **should** use imperative mood. Refer to the subsections below for more details on how to document specific API elements. + +!!! success "**DO** (library code):" + + ```py + def add_ints(a: int, b: int) -> int: + """Add two integers.""" + return a + b + ``` + +!!! failure "**DON'T** (library code):" + + ```py + def add_ints(a: int, b: int) -> int: + """This function adds two integers.""" + return a + b + ``` + +!!! failure "**DON'T** (library code):" + + ```py + def add_ints(a: int, b: int) -> int: + """Adds two integers.""" + return a + b + ``` + +### Modules + +All modules should have + +* a one-line description ([short summary][short-summary-section]), +* a longer description if needed ([extended summary][extended-summary-section]). + +Example: + +```py +"""Containers for tabular data.""" +``` + +### Classes + +All classes should have + +* a one-line description ([short summary][short-summary-section]), +* a longer description if needed ([extended summary][extended-summary-section]) +* a description of the parameters of their `__init__` method ([`Parameters` section][parameters-section]), +* examples that show how to use them correctly ([`Examples` section][examples-section]). + +Example: + +```py +""" +A row is a collection of named values. + +Parameters +---------- +data : Mapping[str, Any] | None + The data. If None, an empty row is created. + +Examples +-------- +>>> from safeds.data.tabular.containers import Row +>>> row = Row({"a": 1, "b": 2}) +""" +``` + +### Functions + +All functions should have + +* a one-line description ([short summary][short-summary-section]), +* a longer description if needed ([extended summary][extended-summary-section]) +* a description of their parameters ([`Parameters` section][parameters-section]), +* a description of their results ([`Returns` section][returns-section]), +* a description of any exceptions that may be raised and under which conditions that may happen ([`Raises` section][raises-section]), +* a description of any warnings that may be issued and under which conditions that may happen ([`Warns` section][warns-section]), +* examples that show how to use them correctly ([`Examples` section][examples-section]). + +Example: + +```py +""" +Return the value of a specified column. + +Parameters +---------- +column_name : str + The column name. + +Returns +------- +value : Any + The column value. + +Raises +------ +UnknownColumnNameError + If the row does not contain the specified column. + +Examples +-------- +>>> from safeds.data.tabular.containers import Row +>>> row = Row({"a": 1, "b": 2}) +>>> row.get_value("a") +1 +""" +``` + +## Tests + +We aim for 100% line coverage, +so automated tests should be added +for any new function. + +### File structure + +Tests belong in the [`tests`][tests-folder] folder. +The file structure in the tests folder +should mirror the file structure +of the [`src`][src-folder] folder. + +### Naming + +Names of test functions +shall start with `test_should_` +followed by a description +of the expected behaviour, +e.g. `test_should_add_column`. + +!!! success "**DO** (library code):" + + ```py + def test_should_raise_if_less_than_or_equal_to_0(self, number_of_trees) -> None: + with pytest.raises(ValueError, match="The parameter 'number_of_trees' has to be greater than 0."): + ... + ``` + +!!! failure "**DON'T** (library code):" + + ```py + def test_value_error(self, number_of_trees) -> None: + with pytest.raises(ValueError, match="The parameter 'number_of_trees' has to be greater than 0."): + ... + ``` + +### Parametrization + +Tests should be parametrized +using `@pytest.mark.parametrize`, +even if there is only a single test case. +This makes it easier +to add new test cases in the future. +Test cases should be given +descriptive IDs. + +!!! success "**DO** (library code):" + + ```py + @pytest.mark.parametrize("number_of_trees", [0, -1], ids=["zero", "negative"]) + def test_should_raise_if_less_than_or_equal_to_0(self, number_of_trees) -> None: + with pytest.raises(ValueError, match="The parameter 'number_of_trees' has to be greater than 0."): + RandomForest(number_of_trees=number_of_trees) + ``` + +!!! failure "**DON'T** (library code):" + + ```py + def test_should_raise_if_less_than_0(self, number_of_trees) -> None: + with pytest.raises(ValueError, match="The parameter 'number_of_trees' has to be greater than 0."): + RandomForest(number_of_trees=-1) + + def test_should_raise_if_equal_to_0(self, number_of_trees) -> None: + with pytest.raises(ValueError, match="The parameter 'number_of_trees' has to be greater than 0."): + RandomForest(number_of_trees=0) + ``` + +## Code style + +### Consistency + +If there is more than one way +to solve a particular task, +check how it has been solved +at other places in the codebase +and stick to that solution. + +### Sort exported classes in `__init__.py` + +Classes defined in a module +that other classes shall be able to import +must be defined +in a list named `__all__` +in the module's `__init__.py` file. +This list should be sorted alphabetically, +to reduce the likelihood of merge conflicts +when adding new classes to it. + +!!! success "**DO** (library code):" + + ```py + __all__ = [ + "Column", + "Row", + "Table", + "TaggedTable", + ] + ``` + +!!! failure "**DON'T** (library code):" + + ```py + __all__ = [ + "Table", + "TaggedTable", + "Column", + "Row", + ] + ``` + +[src-folder]: https://github.com/Safe-DS/Stdlib/tree/main/src +[tests-folder]: https://github.com/Safe-DS/Stdlib/tree/main/tests + +[short-summary-section]: https://numpydoc.readthedocs.io/en/latest/format.html#short-summary +[extended-summary-section]: https://numpydoc.readthedocs.io/en/latest/format.html#extended-summary +[parameters-section]: https://numpydoc.readthedocs.io/en/latest/format.html#parameters +[returns-section]: https://numpydoc.readthedocs.io/en/latest/format.html#returns +[raises-section]: https://numpydoc.readthedocs.io/en/latest/format.html#raises +[warns-section]: https://numpydoc.readthedocs.io/en/latest/format.html#warns +[examples-section]: https://numpydoc.readthedocs.io/en/latest/format.html#examples diff --git a/docs/glossary.md b/docs/glossary.md new file mode 100644 index 000000000..f0afeb80e --- /dev/null +++ b/docs/glossary.md @@ -0,0 +1,139 @@ +# Glossary + +## Accuracy +The fraction of predictions a [classification](#classification) model has correctly identified. Formula: + +$$ +\text{accuracy} = \frac{\text{True Positives + True Negatives}}{\text{Total amount of data points}} +$$ + +See here for respective definitions: +[True Positives](#true-positive-tp) +[True Negatives](#true-negative-tn) + +## Application Programming Interface (API) +An API allows independent applications to communicate with each other and exchange data. + +## Classification +Classification refers to dividing a data set into multiple chunks, which are then considered "classes". + +## Confusion Matrix +A confusion matrix is a table that is used to define the performance of a [classification](#classification) algorithm. +It classifies the predictions to be either be [true positive](#true-positive-tp), [true negative](#true-negative-tn), +[false positive](#false-positive-fp) or [false negative](#false-negative-fn). + +## Decision Tree +A Decision Tree represents the process of conditional evaluation in a tree diagram. + +Implemented in Safe-DS as [Decision Tree][safeds.ml.classical.classification.DecisionTree]. + +## F1-Score +The harmonic mean of [precision](#precision) and [recall](#recall). Formula: + +$$ +f_1 = 2 \cdot \frac{\text{precision} \cdot \text{recall}}{\text{precision} + \text{recall}} +$$ + +## False Negative (FN) +An outcome is considered to be a false negative, if the data model has mistakenly predicted a value of negative class. + +## False Positive (FP) +An outcome is considered to be a false positive, if the data model has mistakenly predicted a value of positive class. + +## Feature +Each feature represents a measurable piece of data that can be used for analysis. +It is analogous to a column within a table. + +## Linear Regression +Linear Regression is the supervised Machine Learning model in which the model finds the best fit linear line between the independent and dependent variable +i.e. it finds the linear relationship between the dependent and independent variable. + +Implemented in Safe-DS as [LinearRegression][safeds.ml.classical.regression.LinearRegression]. + +## Machine Learning (ML) +Machine Learning is a generic term for artificially generating knowledge through experience. +To achieve this, one can choose between a variety of model options. + +## Metric +A data metric is an aggregated calculation within a raw dataset. + +## One Hot Encoder +If a column's entries consist of a non-numerical data type, using a One Hot Encoder will create +a new column for each different entry, filling it with a '1' in the respective places, '0' otherwise. + +Implemented in Safe-DS as [OneHotEncoder][safeds.data.tabular.transformation.OneHotEncoder]. + +## Overfitting +Overfitting is a scenario in which a data model is unable to capture the relationship between the input and output variables accurately, +due to not generalizing enough. + +## Positive Class +The "Positive Class" consists of all attributes to be considered positive. Consequently, every attribute to not be in this class is considered to be negative class. + +## Precision +The ability of a [classification](#classification) model to identify only the relevant data points. Formula: + +$$ +\text{precision} = \frac{\text{True Positives}}{\text{True Positives + False Positives}} +$$ + +See here for respective references: +[True Positives](#true-positive-tp) +[False Positives](#false-positive-fp) + +## Random Forest +Random Forest is an ML model that works by generating decision trees at random. + +Implemented in Safe-DS as [RandomForest][safeds.ml.classical.regression.RandomForest]. + +## Recall +The ability of a [classification](#classification) model to identify all the relevant data points. Formula: + +$$ +\text{recall} = \frac{\text{True Positives}}{\text{True Positives + False Negatives}} +$$ + +See here for respective references: +[True Positives](#true-positive-tp) +[False Negatives](#false-negative-fn) + +## Regression +Regression refers to the estimation of continuous dependent variables. + +## Regularization +Regularization refers to techniques that are used to calibrate machine learning models +in order to minimize the adjusted loss function and prevent [overfitting](#overfitting) or [underfitting](#underfitting). + +## Sample +A sample is a subset of the whole data set. +It is analyzed to uncover the meaningful information in the larger data set. + +## Supervised Learning +Supervised Learning is a subcategory of ML. This approach uses algorithms to learn given data. +Those Algorithms might be able to find hidden meaning in data - without being told where to look. + +## Tagged Table +In addition to a regular table, a Tagged Table will mark one column as tagged, meaning that +an applied algorithm will train to predict its entries. The marked column is referred to as ["target"](#target). + +## Target +The target variable of a dataset is the feature of a dataset about which you want to gain a deeper understanding. + +## Test Set +A set of examples used only to assess the performance of a fully-specified [classifier](#classification). + +## Training Set +A set of examples used for learning, that is to fit the parameters of the [classifier](#classification). + +## True Negative (TN) +An outcome is considered to be a true negative, if the data model has correctly predicted a value of negative class. + +## True Positive (TP) +An outcome is considered to be a true positive, if the data model has correctly predicted a value of positive class. + +## Underfitting +Underfitting is a scenario in which a data model is unable to capture the relationship between the input and output variables accurately, +due to generalizing too much. + +## Validation Set +A set of examples used to the parameters of a [classifier](#classification). diff --git a/docs/tutorials/data_processing.ipynb b/docs/tutorials/data_processing.ipynb index bed2c6280..e005bd609 100644 --- a/docs/tutorials/data_processing.ipynb +++ b/docs/tutorials/data_processing.ipynb @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "outputs": [], "source": [ "from safeds.data.tabular.containers import Table\n", @@ -46,8 +46,18 @@ }, { "cell_type": "code", - "execution_count": null, - "outputs": [], + "execution_count": 28, + "outputs": [ + { + "data": { + "text/plain": " id name sex age \\\n0 0 Abbing, Mr. Anthony male 42.0 \n1 1 Abbott, Master. Eugene Joseph male 13.0 \n2 2 Abbott, Mr. Rossmore Edward male 16.0 \n3 3 Abbott, Mrs. Stanton (Rosa Hunt) female 35.0 \n4 4 Abelseth, Miss. Karen Marie female 16.0 \n5 5 Abelseth, Mr. Olaus Jorgensen male 25.0 \n6 6 Abelson, Mr. Samuel male 30.0 \n7 7 Abelson, Mrs. Samuel (Hannah Wizosky) female 28.0 \n8 8 Abrahamsson, Mr. Abraham August Johannes male 20.0 \n9 9 Abrahim, Mrs. Joseph (Sophie Halaut Easu) female 18.0 \n\n siblings_spouses parents_children ticket travel_class \\\n0 0 0 C.A. 5547 3 \n1 0 2 C.A. 2673 3 \n2 1 1 C.A. 2673 3 \n3 1 1 C.A. 2673 3 \n4 0 0 348125 3 \n5 0 0 348122 3 \n6 1 0 P/PP 3381 2 \n7 1 0 P/PP 3381 2 \n8 0 0 SOTON/O2 3101284 3 \n9 0 0 2657 3 \n\n fare cabin port_embarked survived \n0 7.5500 NaN Southampton 0 \n1 20.2500 NaN Southampton 0 \n2 20.2500 NaN Southampton 0 \n3 20.2500 NaN Southampton 1 \n4 7.6500 NaN Southampton 1 \n5 7.6500 F G63 Southampton 1 \n6 24.0000 NaN Cherbourg 0 \n7 24.0000 NaN Cherbourg 1 \n8 7.9250 NaN Southampton 1 \n9 7.2292 NaN Cherbourg 1 ", + "text/html": "
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11Abbott, Master. Eugene Josephmale13.002C.A. 2673320.2500NaNSouthampton0
22Abbott, Mr. Rossmore Edwardmale16.011C.A. 2673320.2500NaNSouthampton0
33Abbott, Mrs. Stanton (Rosa Hunt)female35.011C.A. 2673320.2500NaNSouthampton1
44Abelseth, Miss. Karen Mariefemale16.00034812537.6500NaNSouthampton1
55Abelseth, Mr. Olaus Jorgensenmale25.00034812237.6500F G63Southampton1
66Abelson, Mr. Samuelmale30.010P/PP 3381224.0000NaNCherbourg0
77Abelson, Mrs. Samuel (Hannah Wizosky)female28.010P/PP 3381224.0000NaNCherbourg1
88Abrahamsson, Mr. Abraham August Johannesmale20.000SOTON/O2 310128437.9250NaNSouthampton1
99Abrahim, Mrs. Joseph (Sophie Halaut Easu)female18.000265737.2292NaNCherbourg1
\n
" + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "titanic_slice = titanic.slice_rows(end=10)\n", "\n", @@ -68,8 +78,18 @@ }, { "cell_type": "code", - "execution_count": null, - "outputs": [], + "execution_count": 29, + "outputs": [ + { + "data": { + "text/plain": "Row({\n 'id': 0,\n 'name': 'Abbing, Mr. Anthony',\n 'sex': 'male',\n 'age': 42.0,\n 'siblings_spouses': 0,\n 'parents_children': 0,\n 'ticket': 'C.A. 5547',\n 'travel_class': 3,\n 'fare': 7.55,\n 'cabin': nan,\n 'port_embarked': 'Southampton',\n 'survived': 0\n})", + "text/html": "
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idnamesexagesiblings_spousesparents_childrentickettravel_classfarecabinport_embarkedsurvived
00Abbing, Mr. Anthonymale42.000C.A. 554737.55NaNSouthampton0
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" + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "titanic_slice.get_row(0)" ], @@ -88,8 +108,18 @@ }, { "cell_type": "code", - "execution_count": null, - "outputs": [], + "execution_count": 30, + "outputs": [ + { + "data": { + "text/plain": "Column('name', ['Abbing, Mr. Anthony', 'Abbott, Master. Eugene Joseph', 'Abbott, Mr. Rossmore Edward', 'Abbott, Mrs. Stanton (Rosa Hunt)', 'Abelseth, Miss. Karen Marie', 'Abelseth, Mr. Olaus Jorgensen', 'Abelson, Mr. Samuel', 'Abelson, Mrs. Samuel (Hannah Wizosky)', 'Abrahamsson, Mr. Abraham August Johannes', 'Abrahim, Mrs. Joseph (Sophie Halaut Easu)'])", + "text/html": "
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6Abelson, Mr. Samuel
7Abelson, Mrs. Samuel (Hannah Wizosky)
8Abrahamsson, Mr. Abraham August Johannes
9Abrahim, Mrs. Joseph (Sophie Halaut Easu)
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" + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "titanic_slice.get_column(\"name\")" ], @@ -108,8 +138,18 @@ }, { "cell_type": "code", - "execution_count": null, - "outputs": [], + "execution_count": 31, + "outputs": [ + { + "data": { + "text/plain": " id name sex age siblings_spouses \\\n0 0 Abbing, Mr. Anthony male 42.0 0 \n1 1 Abbott, Master. Eugene Joseph male 13.0 0 \n\n parents_children ticket travel_class fare cabin port_embarked \\\n0 0 C.A. 5547 3 7.55 NaN Southampton \n1 2 C.A. 2673 3 20.25 NaN Southampton \n\n survived \n0 0 \n1 0 ", + "text/html": "
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00Abbing, Mr. Anthonymale42.000C.A. 554737.55NaNSouthampton0
11Abbott, Master. Eugene Josephmale13.002C.A. 2673320.25NaNSouthampton0
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2Abbott, Mr. Rossmore Edward16.0
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7Abelson, Mrs. Samuel (Hannah Wizosky)28.0
8Abrahamsson, Mr. Abraham August Johannes20.0
9Abrahim, Mrs. Joseph (Sophie Halaut Easu)18.0
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9Abrahim, Mrs. Joseph (Sophie Halaut Easu)1
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" + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "titanic_slice.keep_only_columns([\"name\", \"survived\"])" ], @@ -201,8 +271,18 @@ }, { "cell_type": "code", - "execution_count": null, - "outputs": [], + "execution_count": 35, + "outputs": [ + { + "data": { + "text/plain": " age cabin fare id name \\\n0 42.0 NaN 7.5500 0 Abbing, Mr. Anthony \n1 13.0 NaN 20.2500 1 Abbott, Master. Eugene Joseph \n2 16.0 NaN 20.2500 2 Abbott, Mr. Rossmore Edward \n3 35.0 NaN 20.2500 3 Abbott, Mrs. Stanton (Rosa Hunt) \n4 16.0 NaN 7.6500 4 Abelseth, Miss. Karen Marie \n5 25.0 F G63 7.6500 5 Abelseth, Mr. Olaus Jorgensen \n6 30.0 NaN 24.0000 6 Abelson, Mr. Samuel \n7 28.0 NaN 24.0000 7 Abelson, Mrs. Samuel (Hannah Wizosky) \n8 20.0 NaN 7.9250 8 Abrahamsson, Mr. Abraham August Johannes \n9 18.0 NaN 7.2292 9 Abrahim, Mrs. Joseph (Sophie Halaut Easu) \n\n parents_children port_embarked sex siblings_spouses survived \\\n0 0 Southampton male 0 0 \n1 2 Southampton male 0 0 \n2 1 Southampton male 1 0 \n3 1 Southampton female 1 1 \n4 0 Southampton female 0 1 \n5 0 Southampton male 0 1 \n6 0 Cherbourg male 1 0 \n7 0 Cherbourg female 1 1 \n8 0 Southampton male 0 1 \n9 0 Cherbourg female 0 1 \n\n ticket travel_class \n0 C.A. 5547 3 \n1 C.A. 2673 3 \n2 C.A. 2673 3 \n3 C.A. 2673 3 \n4 348125 3 \n5 348122 3 \n6 P/PP 3381 2 \n7 P/PP 3381 2 \n8 SOTON/O2 3101284 3 \n9 2657 3 ", + "text/html": "
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agecabinfareidnameparents_childrenport_embarkedsexsiblings_spousessurvivedtickettravel_class
042.0NaN7.55000Abbing, Mr. Anthony0Southamptonmale00C.A. 55473
113.0NaN20.25001Abbott, Master. Eugene Joseph2Southamptonmale00C.A. 26733
216.0NaN20.25002Abbott, Mr. Rossmore Edward1Southamptonmale10C.A. 26733
335.0NaN20.25003Abbott, Mrs. Stanton (Rosa Hunt)1Southamptonfemale11C.A. 26733
416.0NaN7.65004Abelseth, Miss. Karen Marie0Southamptonfemale013481253
525.0F G637.65005Abelseth, Mr. Olaus Jorgensen0Southamptonmale013481223
630.0NaN24.00006Abelson, Mr. Samuel0Cherbourgmale10P/PP 33812
728.0NaN24.00007Abelson, Mrs. Samuel (Hannah Wizosky)0Cherbourgfemale11P/PP 33812
820.0NaN7.92508Abrahamsson, Mr. Abraham August Johannes0Southamptonmale01SOTON/O2 31012843
918.0NaN7.22929Abrahim, Mrs. Joseph (Sophie Halaut Easu)0Cherbourgfemale0126573
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" + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "titanic_slice.sort_columns()" ], @@ -221,8 +301,18 @@ }, { "cell_type": "code", - "execution_count": null, - "outputs": [], + "execution_count": 36, + "outputs": [ + { + "data": { + "text/plain": " travel_class ticket survived siblings_spouses sex \\\n0 3 C.A. 5547 0 0 male \n1 3 C.A. 2673 0 0 male \n2 3 C.A. 2673 0 1 male \n3 3 C.A. 2673 1 1 female \n4 3 348125 1 0 female \n5 3 348122 1 0 male \n6 2 P/PP 3381 0 1 male \n7 2 P/PP 3381 1 1 female \n8 3 SOTON/O2 3101284 1 0 male \n9 3 2657 1 0 female \n\n port_embarked parents_children name \\\n0 Southampton 0 Abbing, Mr. Anthony \n1 Southampton 2 Abbott, Master. Eugene Joseph \n2 Southampton 1 Abbott, Mr. Rossmore Edward \n3 Southampton 1 Abbott, Mrs. Stanton (Rosa Hunt) \n4 Southampton 0 Abelseth, Miss. Karen Marie \n5 Southampton 0 Abelseth, Mr. Olaus Jorgensen \n6 Cherbourg 0 Abelson, Mr. Samuel \n7 Cherbourg 0 Abelson, Mrs. Samuel (Hannah Wizosky) \n8 Southampton 0 Abrahamsson, Mr. Abraham August Johannes \n9 Cherbourg 0 Abrahim, Mrs. Joseph (Sophie Halaut Easu) \n\n id fare cabin age \n0 0 7.5500 NaN 42.0 \n1 1 20.2500 NaN 13.0 \n2 2 20.2500 NaN 16.0 \n3 3 20.2500 NaN 35.0 \n4 4 7.6500 NaN 16.0 \n5 5 7.6500 F G63 25.0 \n6 6 24.0000 NaN 30.0 \n7 7 24.0000 NaN 28.0 \n8 8 7.9250 NaN 20.0 \n9 9 7.2292 NaN 18.0 ", + "text/html": "
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travel_classticketsurvivedsiblings_spousessexport_embarkedparents_childrennameidfarecabinage
03C.A. 554700maleSouthampton0Abbing, Mr. Anthony07.5500NaN42.0
13C.A. 267300maleSouthampton2Abbott, Master. Eugene Joseph120.2500NaN13.0
23C.A. 267301maleSouthampton1Abbott, Mr. Rossmore Edward220.2500NaN16.0
33C.A. 267311femaleSouthampton1Abbott, Mrs. Stanton (Rosa Hunt)320.2500NaN35.0
4334812510femaleSouthampton0Abelseth, Miss. Karen Marie47.6500NaN16.0
5334812210maleSouthampton0Abelseth, Mr. Olaus Jorgensen57.6500F G6325.0
62P/PP 338101maleCherbourg0Abelson, Mr. Samuel624.0000NaN30.0
72P/PP 338111femaleCherbourg0Abelson, Mrs. Samuel (Hannah Wizosky)724.0000NaN28.0
83SOTON/O2 310128410maleSouthampton0Abrahamsson, Mr. Abraham August Johannes87.9250NaN20.0
93265710femaleCherbourg0Abrahim, Mrs. Joseph (Sophie Halaut Easu)97.2292NaN18.0
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" + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "titanic_slice.sort_columns(\n", " lambda column1, column2:\n", @@ -244,8 +334,18 @@ }, { "cell_type": "code", - "execution_count": null, - "outputs": [], + "execution_count": 37, + "outputs": [ + { + "data": { + "text/plain": " id name sex age \\\n0 105 Beavan, Mr. William Thomas male 19.0 \n1 191 Canavan, Miss. Mary female 21.0 \n2 192 Canavan, Mr. Patrick male 21.0 \n3 267 Cor, Mr. Ivan male 27.0 \n4 308 Davies, Mr. Evan male 22.0 \n5 333 Devaney, Miss. Margaret Delia female 19.0 \n6 338 Dimic, Mr. Jovan male 42.0 \n7 383 Evans, Miss. Edith Corse female 36.0 \n8 586 Ivanoff, Mr. Kanio male NaN \n9 589 Jalsevac, Mr. Ivan male 29.0 \n10 811 Mineff, Mr. Ivan male 24.0 \n11 916 O'Sullivan, Miss. Bridget Mary female NaN \n12 918 Ovies y Rodriguez, Mr. Servando male 28.5 \n13 990 Rasmussen, Mrs. (Lena Jacobsen Solvang) female NaN \n14 1143 Staneff, Mr. Ivan male NaN \n15 1144 Stankovic, Mr. Ivan male 33.0 \n16 1161 Strilic, Mr. Ivan male 27.0 \n17 1215 van Billiard, Master. James William male NaN \n18 1216 van Billiard, Master. Walter John male 11.5 \n19 1217 van Billiard, Mr. Austin Blyler male 40.5 \n20 1222 van Melkebeke, Mr. Philemon male NaN \n\n siblings_spouses parents_children ticket travel_class fare \\\n0 0 0 323951 3 8.0500 \n1 0 0 364846 3 7.7500 \n2 0 0 364858 3 7.7500 \n3 0 0 349229 3 7.8958 \n4 0 0 SC/A4 23568 3 8.0500 \n5 0 0 330958 3 7.8792 \n6 0 0 315088 3 8.6625 \n7 0 0 PC 17531 1 31.6792 \n8 0 0 349201 3 7.8958 \n9 0 0 349240 3 7.8958 \n10 0 0 349233 3 7.8958 \n11 0 0 330909 3 7.6292 \n12 0 0 PC 17562 1 27.7208 \n13 0 0 65305 3 8.1125 \n14 0 0 349208 3 7.8958 \n15 0 0 349239 3 8.6625 \n16 0 0 315083 3 8.6625 \n17 1 1 A/5. 851 3 14.5000 \n18 1 1 A/5. 851 3 14.5000 \n19 0 2 A/5. 851 3 14.5000 \n20 0 0 345777 3 9.5000 \n\n cabin port_embarked survived \n0 NaN Southampton 0 \n1 NaN Queenstown 0 \n2 NaN Queenstown 0 \n3 NaN Southampton 0 \n4 NaN Southampton 0 \n5 NaN Queenstown 1 \n6 NaN Southampton 0 \n7 A29 Cherbourg 0 \n8 NaN Southampton 0 \n9 NaN Cherbourg 1 \n10 NaN Southampton 0 \n11 NaN Queenstown 0 \n12 D43 Cherbourg 0 \n13 NaN Southampton 0 \n14 NaN Southampton 0 \n15 NaN Cherbourg 0 \n16 NaN Southampton 0 \n17 NaN Southampton 0 \n18 NaN Southampton 0 \n19 NaN Southampton 0 \n20 NaN Southampton 0 ", + "text/html": "
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idnamesexagesiblings_spousesparents_childrentickettravel_classfarecabinport_embarkedsurvived
0105Beavan, Mr. William Thomasmale19.00032395138.0500NaNSouthampton0
1191Canavan, Miss. Maryfemale21.00036484637.7500NaNQueenstown0
2192Canavan, Mr. Patrickmale21.00036485837.7500NaNQueenstown0
3267Cor, Mr. Ivanmale27.00034922937.8958NaNSouthampton0
4308Davies, Mr. Evanmale22.000SC/A4 2356838.0500NaNSouthampton0
5333Devaney, Miss. Margaret Deliafemale19.00033095837.8792NaNQueenstown1
6338Dimic, Mr. Jovanmale42.00031508838.6625NaNSouthampton0
7383Evans, Miss. Edith Corsefemale36.000PC 17531131.6792A29Cherbourg0
8586Ivanoff, Mr. KaniomaleNaN0034920137.8958NaNSouthampton0
9589Jalsevac, Mr. Ivanmale29.00034924037.8958NaNCherbourg1
10811Mineff, Mr. Ivanmale24.00034923337.8958NaNSouthampton0
11916O'Sullivan, Miss. Bridget MaryfemaleNaN0033090937.6292NaNQueenstown0
12918Ovies y Rodriguez, Mr. Servandomale28.500PC 17562127.7208D43Cherbourg0
13990Rasmussen, Mrs. (Lena Jacobsen Solvang)femaleNaN006530538.1125NaNSouthampton0
141143Staneff, Mr. IvanmaleNaN0034920837.8958NaNSouthampton0
151144Stankovic, Mr. Ivanmale33.00034923938.6625NaNCherbourg0
161161Strilic, Mr. Ivanmale27.00031508338.6625NaNSouthampton0
171215van Billiard, Master. James WilliammaleNaN11A/5. 851314.5000NaNSouthampton0
181216van Billiard, Master. Walter Johnmale11.511A/5. 851314.5000NaNSouthampton0
191217van Billiard, Mr. Austin Blylermale40.502A/5. 851314.5000NaNSouthampton0
201222van Melkebeke, Mr. PhilemonmaleNaN0034577739.5000NaNSouthampton0
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" + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "titanic.filter_rows(\n", " lambda row:\n", @@ -255,6 +355,239 @@ "metadata": { "collapsed": false } + }, + { + "cell_type": "markdown", + "source": [ + "# Transform table\n", + "1. Transform table using `Imputer`. `Imputer`s replace missing values with other values (e.g. a constant, the mean or the median of the column etc.) depending on the chosen startegy, for example, the following `Imputer` will replace missing values in the table with the constant 0:" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 38, + "outputs": [ + { + "data": { + "text/plain": " id name sex age siblings_spouses \\\n0 0 Abbing, Mr. Anthony male 42.0 0 \n1 1 Abbott, Master. Eugene Joseph male 13.0 0 \n2 2 Abbott, Mr. Rossmore Edward male 16.0 1 \n3 3 Abbott, Mrs. Stanton (Rosa Hunt) female 35.0 1 \n4 4 Abelseth, Miss. Karen Marie female 16.0 0 \n... ... ... ... ... ... \n1304 1304 Zabour, Miss. Hileni female 14.5 1 \n1305 1305 Zabour, Miss. Thamine female 0 1 \n1306 1306 Zakarian, Mr. Mapriededer male 26.5 0 \n1307 1307 Zakarian, Mr. Ortin male 27.0 0 \n1308 1308 Zimmerman, Mr. Leo male 29.0 0 \n\n parents_children ticket travel_class fare cabin port_embarked \\\n0 0 C.A. 5547 3 7.55 0 Southampton \n1 2 C.A. 2673 3 20.25 0 Southampton \n2 1 C.A. 2673 3 20.25 0 Southampton \n3 1 C.A. 2673 3 20.25 0 Southampton \n4 0 348125 3 7.65 0 Southampton \n... ... ... ... ... ... ... \n1304 0 2665 3 14.4542 0 Cherbourg \n1305 0 2665 3 14.4542 0 Cherbourg \n1306 0 2656 3 7.225 0 Cherbourg \n1307 0 2670 3 7.225 0 Cherbourg \n1308 0 315082 3 7.875 0 Southampton \n\n survived \n0 0 \n1 0 \n2 0 \n3 1 \n4 1 \n... ... \n1304 0 \n1305 0 \n1306 0 \n1307 0 \n1308 0 \n\n[1309 rows x 12 columns]", + "text/html": "
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n \n \n \n \n \n \n \n \n
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88Abrahamsson, Mr. Abraham August Johannesmale20.000SOTON/O2 310128437.9250Southampton1
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1010Adahl, Mr. Mauritz Nils Martinmale30.000C 707637.250Southampton0
1111Adams, Mr. Johnmale26.00034182638.050Southampton0
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1313Aks, Master. Philip Frankmale0.83330139209139.350Southampton1
1414Aks, Mrs. Sam (Leah Rosen)female18.00139209139.350Southampton1
1515Albimona, Mr. Nassef Cassemmale26.0002699318.78750Cherbourg1
1616Aldworth, Mr. Charles Augustusmale30.000248744213.00Southampton0
1717Alexander, Mr. Williammale26.000347437.88750Southampton0
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1919Ali, Mr. Ahmedmale24.000SOTON/O.Q. 310131137.050Southampton0
2020Ali, Mr. Williammale25.000SOTON/O.Q. 310131237.050Southampton0
2121Allen, Miss. Elisabeth Waltonfemale29.000241601211.3375B5Southampton1
2222Allen, Mr. William Henrymale35.00037345038.050Southampton0
2323Allison, Master. Hudson Trevormale0.9167121137811151.55C22 C26Southampton1
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2525Allison, Mr. Hudson Joshua Creightonmale30.0121137811151.55C22 C26Southampton0
2626Allison, Mrs. Hudson J C (Bessie Waldo Daniels)female25.0121137811151.55C22 C26Southampton0
2727Allum, Mr. Owen Georgemale18.000222338.30Southampton0
2828Andersen, Mr. Albert Karvinmale32.000C 4001322.5250Southampton0
2929Andersen-Jensen, Miss. Carla Christine Nielsinefemale19.01035004637.85420Southampton1
3030Anderson, Mr. Harrymale48.00019952126.55E12Southampton1
3131Andersson, Master. Sigvard Harald Eliasmale4.042347082331.2750Southampton0
3232Andersson, Miss. Ebba Iris Alfridafemale6.042347082331.2750Southampton0
3333Andersson, Miss. Ellis Anna Mariafemale2.042347082331.2750Southampton0
3434Andersson, Miss. Erna Alexandrafemale17.042310128137.9250Southampton1
3535Andersson, Miss. Ida Augusta Margaretafemale38.04234709137.7750Southampton0
3636Andersson, Miss. Ingeborg Constanziafemale9.042347082331.2750Southampton0
3737Andersson, Miss. Sigrid Elisabethfemale11.042347082331.2750Southampton0
3838Andersson, Mr. Anders Johanmale39.015347082331.2750Southampton0
3939Andersson, Mr. August Edvard ('Wennerstrom')male27.00035004337.79580Southampton1
4040Andersson, Mr. Johan Samuelmale26.00034707537.7750Southampton0
4141Andersson, Mrs. Anders Johan (Alfrida Konstant...female39.015347082331.2750Southampton0
4242Andreasson, Mr. Paul Edvinmale20.00034746637.85420Southampton0
4343Andrew, Mr. Edgardo Samuelmale18.000231945211.50Southampton0
4444Andrew, Mr. Frank Thomasmale25.000C.A. 34050210.50Southampton0
4545Andrews, Miss. Kornelia Theodosiafemale63.01013502177.9583D7Southampton1
4646Andrews, Mr. Thomas Jrmale39.00011205010.0A36Southampton0
4747Angheloff, Mr. Minkomale26.00034920237.89580Southampton0
4848Angle, Mr. William Amale34.010226875226.00Southampton0
4949Angle, Mrs. William A (Florence 'Mary' Agnes H...female36.010226875226.00Southampton1
5050Appleton, Mrs. Edward Dale (Charlotte Lamson)female53.02011769151.4792C101Southampton1
5151Arnold-Franchi, Mr. Josefmale25.010349237317.80Southampton0
5252Arnold-Franchi, Mrs. Josef (Josefine Franchi)female18.010349237317.80Southampton0
5353Aronsson, Mr. Ernst Axel Algotmale24.00034991137.7750Southampton0
5454Artagaveytia, Mr. Ramonmale71.000PC 17609149.50420Cherbourg0
5555Ashby, Mr. Johnmale57.000244346213.00Southampton0
5656Asim, Mr. Adolamale35.000SOTON/O.Q. 310131037.050Southampton0
5757Asplund, Master. Carl Edgarmale5.042347077331.38750Southampton0
5858Asplund, Master. Clarence Gustaf Hugomale9.042347077331.38750Southampton0
5959Asplund, Master. Edvin Rojj Felixmale3.042347077331.38750Southampton1
6060Asplund, Master. Filip Oscarmale13.042347077331.38750Southampton0
6161Asplund, Miss. Lillian Gertrudfemale5.042347077331.38750Southampton1
6262Asplund, Mr. Carl Oscar Vilhelm Gustafssonmale40.015347077331.38750Southampton0
6363Asplund, Mr. Johan Charlesmale23.00035005437.79580Southampton1
6464Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...female38.015347077331.38750Southampton1
6565Assaf Khalil, Mrs. Mariana ('Miriam')female45.000269637.2250Cherbourg1
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6767Assam, Mr. Alimale23.000SOTON/O.Q. 310130937.050Southampton0
6868Astor, Col. John Jacobmale47.010PC 177571227.525C62 C64Cherbourg0
6969Astor, Mrs. John Jacob (Madeleine Talmadge Force)female18.010PC 177571227.525C62 C64Cherbourg1
7070Attalah, Miss. Malakefemale17.0002627314.45830Cherbourg0
7171Attalah, Mr. Sleimanmale30.000269437.2250Cherbourg0
7272Aubart, Mme. Leontine Paulinefemale24.000PC 17477169.3B35Cherbourg1
7373Augustsson, Mr. Albertmale23.00034746837.85420Southampton0
7474Ayoub, Miss. Banourafemale13.000268737.22920Cherbourg1
7575Baccos, Mr. Raffullmale20.000267937.2250Cherbourg0
7676Backstrom, Mr. Karl Alfredmale32.0103101278315.850Southampton0
7777Backstrom, Mrs. Karl Alfred (Maria Mathilda Gu...female33.0303101278315.850Southampton1
7878Baclini, Miss. Eugeniefemale0.75212666319.25830Cherbourg1
7979Baclini, Miss. Helene Barbarafemale0.75212666319.25830Cherbourg1
8080Baclini, Miss. Marie Catherinefemale5.0212666319.25830Cherbourg1
8181Baclini, Mrs. Solomon (Latifa Qurban)female24.0032666319.25830Cherbourg1
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8484Bailey, Mr. Percy Andrewmale18.00029108211.50Southampton0
8585Baimbrigge, Mr. Charles Robertmale23.000C.A. 31030210.50Southampton0
8686Balkic, Mr. Cerinmale26.00034924837.89580Southampton0
8787Ball, Mrs. (Ada E Hall)female36.00028551213.0DSouthampton1
8888Banfield, Mr. Frederick Jamesmale28.000C.A./SOTON 34068210.50Southampton0
8989Barah, Mr. Hanna Assimale20.000266337.22920Cherbourg1
9090Barbara, Miss. Saiidefemale18.0012691314.45420Cherbourg0
9191Barbara, Mrs. (Catherine David)female45.0012691314.45420Cherbourg0
9292Barber, Miss. Ellen 'Nellie'female26.00019877178.850Southampton1
9393Barkworth, Mr. Algernon Henry Wilsonmale80.00027042130.0A23Southampton1
9494Barry, Miss. Juliafemale27.00033084437.87920Queenstown0
9595Barton, Mr. David Johnmale22.00032466938.050Southampton0
9696Bateman, Rev. Robert Jamesmale51.000S.O.P. 1166212.5250Southampton0
9797Baumann, Mr. John Dmale000PC 17318125.9250Southampton0
9898Baxter, Mr. Quigg Edmondmale24.001PC 175581247.5208B58 B60Cherbourg0
9999Baxter, Mrs. James (Helene DeLaudeniere Chaput)female50.001PC 175581247.5208B58 B60Cherbourg1
100100Bazzani, Miss. Albinafemale32.00011813176.2917D15Cherbourg1
101101Beane, Mr. Edwardmale32.0102908226.00Southampton1
102102Beane, Mrs. Edward (Ethel Clarke)female19.0102908226.00Southampton1
103103Beattie, Mr. Thomsonmale36.00013050175.2417C6Cherbourg0
104104Beauchamp, Mr. Henry Jamesmale28.000244358226.00Southampton0
105105Beavan, Mr. William Thomasmale19.00032395138.050Southampton0
106106Becker, Master. Richard Fmale1.021230136239.0F4Southampton1
107107Becker, Miss. Marion Louisefemale4.021230136239.0F4Southampton1
108108Becker, Miss. Ruth Elizabethfemale12.021230136239.0F4Southampton1
109109Becker, Mrs. Allen Oliver (Nellie E Baumgardner)female36.003230136239.0F4Southampton1
110110Beckwith, Mr. Richard Leonardmale37.01111751152.5542D35Southampton1
111111Beckwith, Mrs. Richard Leonard (Sallie Monypeny)female47.01111751152.5542D35Southampton1
112112Beesley, Mr. Lawrencemale34.000248698213.0D56Southampton1
113113Behr, Mr. Karl Howellmale26.000111369130.0C148Cherbourg1
114114Bengtsson, Mr. John Viktormale26.00034706837.7750Southampton0
115115Bentham, Miss. Lilian Wfemale19.00028404213.00Southampton1
116116Berglund, Mr. Karl Ivar Svenmale22.000PP 434839.350Southampton0
117117Berriman, Mr. William Johnmale23.00028425213.00Southampton0
118118Betros, Master. Semanmale000262237.22920Cherbourg0
119119Betros, Mr. Tannousmale20.000264834.01250Cherbourg0
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12711271Wick, Mrs. George Dennick (Mary Hitchcock)female45.011369281164.86670Southampton1
12721272Widegren, Mr. Carl/Charles Petermale51.00034706437.750Southampton0
12731273Widener, Mr. George Duntonmale50.0111135031211.5C80Cherbourg0
12741274Widener, Mr. Harry Elkinsmale27.0021135031211.5C82Cherbourg0
12751275Widener, Mrs. George Dunton (Eleanor Elkins)female50.0111135031211.5C80Cherbourg1
12761276Wiklund, Mr. Jakob Alfredmale18.010310126736.49580Southampton0
12771277Wiklund, Mr. Karl Johanmale21.010310126636.49580Southampton0
12781278Wilhelms, Mr. Charlesmale31.000244270213.00Southampton1
12791279Wilkes, Mrs. James (Ellen Needs)female47.01036327237.00Southampton1
12801280Willard, Miss. Constancefemale21.000113795126.550Southampton1
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12831283Williams, Mr. Charles Duanemale51.001PC 17597161.37920Cherbourg0
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12851285Williams, Mr. Howard Hugh 'Harry'male000A/5 246638.050Southampton0
12861286Williams, Mr. Lesliemale28.50054636316.10Southampton0
12871287Williams, Mr. Richard Norris IImale21.001PC 17597161.37920Cherbourg1
12881288Williams-Lambert, Mr. Fletcher Fellowsmale000113510135.0C128Southampton0
12891289Wilson, Miss. Helen Alicefemale31.000169661134.5E39 E41Cherbourg1
12901290Windelov, Mr. Einarmale21.000SOTON/OQ 310131737.250Southampton0
12911291Wirz, Mr. Albertmale27.00031515438.66250Southampton0
12921292Wiseman, Mr. Phillippemale000A/4. 3424437.250Southampton0
12931293Wittevrongel, Mr. Camillemale36.00034577139.50Southampton0
12941294Woolner, Mr. Hughmale00019947135.5C52Southampton1
12951295Wright, Miss. Marionfemale26.000220844213.50Southampton1
12961296Wright, Mr. Georgemale62.000113807126.550Southampton0
12971297Yasbeck, Mr. Antonimale27.0102659314.45420Cherbourg0
12981298Yasbeck, Mrs. Antoni (Selini Alexander)female15.0102659314.45420Cherbourg1
12991299Young, Miss. Marie Gricefemale36.000PC 177601135.6333C32Cherbourg1
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13011301Yousif, Mr. Wazlimale000264737.2250Cherbourg0
13021302Yousseff, Mr. Geriousmale0002627314.45830Cherbourg0
13031303Yrois, Miss. Henriette ('Mrs Harbeck')female24.000248747213.00Southampton0
13041304Zabour, Miss. Hilenifemale14.5102665314.45420Cherbourg0
13051305Zabour, Miss. Thaminefemale0102665314.45420Cherbourg0
13061306Zakarian, Mr. Mapriededermale26.500265637.2250Cherbourg0
13071307Zakarian, Mr. Ortinmale27.000267037.2250Cherbourg0
13081308Zimmerman, Mr. Leomale29.00031508237.8750Southampton0
\n
" + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from safeds.data.tabular.transformation import Imputer\n", + "\n", + "imputer = Imputer(Imputer.Strategy.Constant(0))\n", + "transformed_table= imputer.fit_and_transform(titanic)\n", + "\n", + "transformed_table" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "2. Transform table using `LabelEncoder`, this will encode categorical features in the chosen `Column`s as integers:" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 39, + "outputs": [ + { + "data": { + "text/plain": " id name sex age siblings_spouses \\\n0 0 Abbing, Mr. Anthony 1.0 42.0 0 \n1 1 Abbott, Master. Eugene Joseph 1.0 13.0 0 \n2 2 Abbott, Mr. Rossmore Edward 1.0 16.0 1 \n3 3 Abbott, Mrs. Stanton (Rosa Hunt) 0.0 35.0 1 \n4 4 Abelseth, Miss. Karen Marie 0.0 16.0 0 \n... ... ... ... ... ... \n1304 1304 Zabour, Miss. Hileni 0.0 14.5 1 \n1305 1305 Zabour, Miss. Thamine 0.0 NaN 1 \n1306 1306 Zakarian, Mr. Mapriededer 1.0 26.5 0 \n1307 1307 Zakarian, Mr. Ortin 1.0 27.0 0 \n1308 1308 Zimmerman, Mr. Leo 1.0 29.0 0 \n\n parents_children ticket travel_class fare cabin port_embarked \\\n0 0 C.A. 5547 3 7.5500 NaN 2.0 \n1 2 C.A. 2673 3 20.2500 NaN 2.0 \n2 1 C.A. 2673 3 20.2500 NaN 2.0 \n3 1 C.A. 2673 3 20.2500 NaN 2.0 \n4 0 348125 3 7.6500 NaN 2.0 \n... ... ... ... ... ... ... \n1304 0 2665 3 14.4542 NaN 0.0 \n1305 0 2665 3 14.4542 NaN 0.0 \n1306 0 2656 3 7.2250 NaN 0.0 \n1307 0 2670 3 7.2250 NaN 0.0 \n1308 0 315082 3 7.8750 NaN 2.0 \n\n survived \n0 0 \n1 0 \n2 0 \n3 1 \n4 1 \n... ... \n1304 0 \n1305 0 \n1306 0 \n1307 0 \n1308 0 \n\n[1309 rows x 12 columns]", + "text/html": "
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
idnamesexagesiblings_spousesparents_childrentickettravel_classfarecabinport_embarkedsurvived
00Abbing, Mr. Anthony1.042.000000C.A. 554737.5500NaN2.00
11Abbott, Master. Eugene Joseph1.013.000002C.A. 2673320.2500NaN2.00
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13031303Yrois, Miss. Henriette ('Mrs Harbeck')0.024.000000248747213.0000NaN2.00
13041304Zabour, Miss. Hileni0.014.5000102665314.4542NaN0.00
13051305Zabour, Miss. Thamine0.0NaN102665314.4542NaN0.00
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13081308Zimmerman, Mr. Leo1.029.00000031508237.8750NaN2.00
\n
" + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from safeds.data.tabular.transformation import LabelEncoder\n", + "\n", + "transformed_table = LabelEncoder().fit_and_transform(titanic, [\"sex\", \"port_embarked\"])\n", + "\n", + "transformed_table" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "3. Transform table using `OneHotEncoder`, this will create new `Column`s based on unique values in each chosen `Column`:\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 40, + "outputs": [ + { + "data": { + "text/plain": " id name sex__male sex__female age \\\n0 0 Abbing, Mr. Anthony 1.0 0.0 42.0 \n1 1 Abbott, Master. Eugene Joseph 1.0 0.0 13.0 \n2 2 Abbott, Mr. Rossmore Edward 1.0 0.0 16.0 \n3 3 Abbott, Mrs. Stanton (Rosa Hunt) 0.0 1.0 35.0 \n4 4 Abelseth, Miss. Karen Marie 0.0 1.0 16.0 \n... ... ... ... ... ... \n1304 1304 Zabour, Miss. Hileni 0.0 1.0 14.5 \n1305 1305 Zabour, Miss. Thamine 0.0 1.0 NaN \n1306 1306 Zakarian, Mr. Mapriededer 1.0 0.0 26.5 \n1307 1307 Zakarian, Mr. Ortin 1.0 0.0 27.0 \n1308 1308 Zimmerman, Mr. Leo 1.0 0.0 29.0 \n\n siblings_spouses parents_children ticket travel_class__3 \\\n0 0 0 C.A. 5547 1.0 \n1 0 2 C.A. 2673 1.0 \n2 1 1 C.A. 2673 1.0 \n3 1 1 C.A. 2673 1.0 \n4 0 0 348125 1.0 \n... ... ... ... ... \n1304 1 0 2665 1.0 \n1305 1 0 2665 1.0 \n1306 0 0 2656 1.0 \n1307 0 0 2670 1.0 \n1308 0 0 315082 1.0 \n\n travel_class__2 travel_class__1 fare cabin port_embarked survived \n0 0.0 0.0 7.5500 NaN Southampton 0 \n1 0.0 0.0 20.2500 NaN Southampton 0 \n2 0.0 0.0 20.2500 NaN Southampton 0 \n3 0.0 0.0 20.2500 NaN Southampton 1 \n4 0.0 0.0 7.6500 NaN Southampton 1 \n... ... ... ... ... ... ... \n1304 0.0 0.0 14.4542 NaN Cherbourg 0 \n1305 0.0 0.0 14.4542 NaN Cherbourg 0 \n1306 0.0 0.0 7.2250 NaN Cherbourg 0 \n1307 0.0 0.0 7.2250 NaN Cherbourg 0 \n1308 0.0 0.0 7.8750 NaN Southampton 0 \n\n[1309 rows x 15 columns]", + "text/html": "
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
idnamesex__malesex__femaleagesiblings_spousesparents_childrentickettravel_class__3travel_class__2travel_class__1farecabinport_embarkedsurvived
00Abbing, Mr. Anthony1.00.042.000000C.A. 55471.00.00.07.5500NaNSouthampton0
11Abbott, Master. Eugene Joseph1.00.013.000002C.A. 26731.00.00.020.2500NaNSouthampton0
22Abbott, Mr. Rossmore Edward1.00.016.000011C.A. 26731.00.00.020.2500NaNSouthampton0
33Abbott, Mrs. Stanton (Rosa Hunt)0.01.035.000011C.A. 26731.00.00.020.2500NaNSouthampton1
44Abelseth, Miss. Karen Marie0.01.016.0000003481251.00.00.07.6500NaNSouthampton1
55Abelseth, Mr. Olaus Jorgensen1.00.025.0000003481221.00.00.07.6500F G63Southampton1
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113113Behr, Mr. Karl Howell1.00.026.0000001113690.00.01.030.0000C148Cherbourg1
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164164Brown, Mrs. Thomas William Solomon (Elizabeth ...0.01.040.000011297500.01.00.039.0000NaNSouthampton1
165165Bryhl, Miss. Dagmar Jenny Ingeborg0.01.020.0000102368530.01.00.026.0000NaNSouthampton1
166166Bryhl, Mr. Kurt Arnold Gottfrid1.00.025.0000102368530.01.00.026.0000NaNSouthampton0
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168168Buckley, Mr. Daniel1.00.021.0000003309201.00.00.07.8208NaNQueenstown1
169169Bucknell, Mrs. William Robert (Emma Eliza Ward)0.01.060.000000118130.00.01.076.2917D15Cherbourg1
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174174Butler, Mr. Reginald Fenton1.00.025.0000002346860.01.00.013.0000NaNSouthampton0
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184184Caldwell, Master. Alden Gates1.00.00.8333022487380.01.00.029.0000NaNSouthampton1
185185Caldwell, Mr. Albert Francis1.00.026.0000112487380.01.00.029.0000NaNSouthampton1
186186Caldwell, Mrs. Albert Francis (Sylvia Mae Harb...0.01.022.0000112487380.01.00.029.0000NaNSouthampton1
187187Calic, Mr. Jovo1.00.017.0000003150931.00.00.08.6625NaNSouthampton0
188188Calic, Mr. Petar1.00.017.0000003150861.00.00.08.6625NaNSouthampton0
189189Cameron, Miss. Clear Annie0.01.035.000000F.C.C. 135280.01.00.021.0000NaNSouthampton1
190190Campbell, Mr. William1.00.0NaN002398530.01.00.00.0000NaNSouthampton0
191191Canavan, Miss. Mary0.01.021.0000003648461.00.00.07.7500NaNQueenstown0
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193193Candee, Mrs. Edward (Helen Churchill Hungerford)0.01.053.000000PC 176060.00.01.027.4458NaNCherbourg1
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206206Carrau, Mr. Jose Pedro1.00.017.0000001130590.00.01.047.1000NaNSouthampton0
207207Carter, Master. William Thornton II1.00.011.0000121137600.00.01.0120.0000B96 B98Southampton1
208208Carter, Miss. Lucile Polk0.01.014.0000121137600.00.01.0120.0000B96 B98Southampton1
209209Carter, Mr. William Ernest1.00.036.0000121137600.00.01.0120.0000B96 B98Southampton1
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211211Carter, Mrs. William Ernest (Lucile Polk)0.01.036.0000121137600.00.01.0120.0000B96 B98Southampton1
212212Carter, Rev. Ernest Courtenay1.00.054.0000102442520.01.00.026.0000NaNSouthampton0
213213Carver, Mr. Alfred John1.00.028.0000003920951.00.00.07.2500NaNSouthampton0
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216216Cavendish, Mr. Tyrell William1.00.036.000010198770.00.01.078.8500C46Southampton0
217217Cavendish, Mrs. Tyrell William (Julia Florence...0.01.076.000010198770.00.01.078.8500C46Southampton1
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222222Chambers, Mrs. Norman Campbell (Bertha Griggs)0.01.033.0000101138060.00.01.053.1000E8Southampton1
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231231Chip, Mr. Chang1.00.032.00000016011.00.00.056.4958NaNSouthampton1
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241241Clarke, Mrs. Charles V (Ada Maria Winfield)0.01.028.00001020030.01.00.026.0000NaNSouthampton1
242242Cleaver, Miss. Alice0.01.022.0000001137810.00.01.0151.5500NaNSouthampton1
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244244Coelho, Mr. Domingos Fernandeo1.00.020.000000SOTON/O.Q. 31013071.00.00.07.0500NaNSouthampton0
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262262Connolly, Miss. Kate0.01.030.0000003309721.00.00.07.6292NaNQueenstown0
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265265Cook, Mrs. (Selena Rogers)0.01.022.000000W./C. 142660.01.00.010.5000F33Southampton1
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271271Corn, Mr. Harry1.00.030.000000SOTON/OQ 3920901.00.00.08.0500NaNSouthampton0
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273273Cotterill, Mr. Henry 'Harry'1.00.021.000000291070.01.00.011.5000NaNSouthampton0
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283283Crosby, Miss. Harriet R0.01.036.000002WE/P 57350.00.01.071.0000B22Southampton1
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324324Dean, Mrs. Bertram (Eva Georgetta Light)0.01.033.000012C.A. 23151.00.00.020.5750NaNSouthampton1
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326326del Carlo, Mrs. Sebastiano (Argenia Genovesi)0.01.024.000010SC/PARIS 21670.01.00.027.7208NaNCherbourg1
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336336Dick, Mrs. Albert Adrian (Vera Gillespie)0.01.017.000010174740.00.01.057.0000B20Southampton1
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355355Drazenoic, Mr. Jozef1.00.033.0000003492411.00.00.07.8958NaNCherbourg0
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360360Duff Gordon, Lady. (Lucille Christiana Sutherl...0.01.048.000010117550.00.01.039.6000A16Cherbourg1
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365365Duran y More, Miss. Florentina0.01.030.000010SC/PARIS 21480.01.00.013.8583NaNCherbourg1
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367367Dyker, Mrs. Adolf Fredrik (Anna Elisabeth Judi...0.01.022.0000103470721.00.00.013.9000NaNSouthampton1
368368Earnshaw, Mrs. Boulton (Olive Potter)0.01.023.000001117670.00.01.083.1583C54Cherbourg1
369369Edvardsson, Mr. Gustaf Hjalmar1.00.018.0000003499121.00.00.07.7750NaNSouthampton0
370370Eitemiller, Mr. George Floyd1.00.023.000000297510.01.00.013.0000NaNSouthampton0
371371Eklund, Mr. Hans Linus1.00.016.0000003470741.00.00.07.7750NaNSouthampton0
372372Ekstrom, Mr. Johan1.00.045.0000003470611.00.00.06.9750NaNSouthampton0
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375375Elias, Mr. Joseph Jr1.00.017.00001126901.00.00.07.2292NaNCherbourg0
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382382Eustis, Miss. Elizabeth Mussey0.01.054.000010369470.00.01.078.2667D20Cherbourg1
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384384Everett, Mr. Thomas James1.00.040.500000C.A. 62121.00.00.015.1000NaNSouthampton0
385385Fahlstrom, Mr. Arne Jonas1.00.018.0000002361710.01.00.013.0000NaNSouthampton0
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388388Faunthorpe, Mr. Harry1.00.040.00001029260.01.00.026.0000NaNSouthampton0
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393393Flegenheim, Mrs. Alfred (Antoinette)0.01.0NaN00PC 175980.00.01.031.6833NaNSouthampton1
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420420Frauenthal, Dr. Henry William1.00.050.000020PC 176110.00.01.0133.6500NaNSouthampton1
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422422Frauenthal, Mrs. Henry William (Clara Heinshei...0.01.0NaN10PC 176110.00.01.0133.6500NaNSouthampton1
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474474Graham, Mrs. William Thompson (Edith Junkins)0.01.058.000001PC 175820.00.01.0153.4625C125Southampton1
475475Green, Mr. George Henry1.00.051.000000214401.00.00.08.0500NaNSouthampton0
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507507Harper, Mr. Henry Sleeper1.00.048.000010PC 175720.00.01.076.7292D33Cherbourg1
508508Harper, Mrs. Henry Sleeper (Myna Haxtun)0.01.049.000010PC 175720.00.01.076.7292D33Cherbourg1
509509Harper, Rev. John1.00.028.0000012487270.01.00.033.0000NaNSouthampton0
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513513Harris, Mr. Walter1.00.030.000000W/C 142080.01.00.010.5000NaNSouthampton0
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515515Harrison, Mr. William1.00.040.0000001120590.00.01.00.0000B94Southampton0
516516Hart, Miss. Eva Miriam0.01.07.000002F.C.C. 135290.01.00.026.2500NaNSouthampton1
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521521Hassan, Mr. Houssein G N1.00.011.00000026991.00.00.018.7875NaNCherbourg0
522522Hawksford, Mr. Walter James1.00.0NaN00169880.00.01.030.0000D45Southampton1
523523Hays, Miss. Margaret Bechstein0.01.024.000000117670.00.01.083.1583C54Cherbourg1
524524Hays, Mr. Charles Melville1.00.055.000011127490.00.01.093.5000B69Southampton0
525525Hays, Mrs. Charles Melville (Clara Jennings Gr...0.01.052.000011127490.00.01.093.5000B69Southampton1
526526Head, Mr. Christopher1.00.042.0000001130380.00.01.042.5000B11Southampton0
527527Healy, Miss. Hanora 'Nora'0.01.0NaN003703751.00.00.07.7500NaNQueenstown1
528528Hedman, Mr. Oskar Arvid1.00.027.0000003470891.00.00.06.9750NaNSouthampton1
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532532Heininen, Miss. Wendla Maria0.01.023.000000STON/O2. 31012901.00.00.07.9250NaNSouthampton0
533533Hellstrom, Miss. Hilda Maria0.01.022.00000075481.00.00.08.9625NaNSouthampton1
534534Hendekovic, Mr. Ignjac1.00.028.0000003492431.00.00.07.8958NaNSouthampton0
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538538Herman, Miss. Kate0.01.024.0000122208450.01.00.065.0000NaNSouthampton1
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541541Hewlett, Mrs. (Mary D Kingcome)0.01.055.0000002487060.01.00.016.0000NaNSouthampton1
542542Hickman, Mr. Leonard Mark1.00.024.000020S.O.C. 148790.01.00.073.5000NaNSouthampton0
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546546Hiltunen, Miss. Marta0.01.018.0000112506500.01.00.013.0000NaNSouthampton0
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548548Hippach, Miss. Jean Gertrude0.01.016.0000011113610.00.01.057.9792B18Cherbourg1
549549Hippach, Mrs. Louis Albert (Ida Sophia Fischer)0.01.044.0000011113610.00.01.057.9792B18Cherbourg1
550550Hirvonen, Miss. Hildur E0.01.02.00000131012981.00.00.012.2875NaNSouthampton1
551551Hirvonen, Mrs. Alexander (Helga E Lindqvist)0.01.022.00001131012981.00.00.012.2875NaNSouthampton1
552552Hocking, Miss. Ellen 'Nellie'0.01.020.000021291050.01.00.023.0000NaNSouthampton1
553553Hocking, Mr. Richard George1.00.023.000021291040.01.00.011.5000NaNSouthampton0
554554Hocking, Mr. Samuel James Metcalfe1.00.036.0000002429630.01.00.013.0000NaNSouthampton0
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556556Hodges, Mr. Henry Price1.00.050.0000002506430.01.00.013.0000NaNSouthampton0
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558558Hold, Mr. Stephen1.00.044.000010267070.01.00.026.0000NaNSouthampton0
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568568Hosono, Mr. Masabumi1.00.042.0000002377980.01.00.013.0000NaNSouthampton1
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573573Hoyt, Mr. William Fisher1.00.0NaN00PC 176000.00.01.030.6958NaNCherbourg0
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576576Hunt, Mr. George Henry1.00.033.000000SCO/W 15850.01.00.012.2750NaNSouthampton0
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587587Jacobsohn, Mr. Sidney Samuel1.00.042.0000102438470.01.00.027.0000NaNSouthampton0
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589589Jalsevac, Mr. Ivan1.00.029.0000003492401.00.00.07.8958NaNCherbourg1
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593593Jefferys, Mr. Clifford Thomas1.00.024.000020C.A. 310290.01.00.031.5000NaNSouthampton0
594594Jefferys, Mr. Ernest Wilfred1.00.022.000020C.A. 310290.01.00.031.5000NaNSouthampton0
595595Jenkin, Mr. Stephen Curnow1.00.032.000000C.A. 331110.01.00.010.5000NaNSouthampton0
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597597Jensen, Mr. Niels Peder1.00.048.0000003500471.00.00.07.8542NaNSouthampton0
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603603Johansson Palmquist, Mr. Oskar Leander1.00.026.0000003470701.00.00.07.7750NaNSouthampton1
604604Johansson, Mr. Erik1.00.022.0000003500521.00.00.07.7958NaNSouthampton0
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607607Johansson, Mr. Nils1.00.029.0000003474671.00.00.07.8542NaNSouthampton0
608608Johnson, Master. Harold Theodor1.00.04.0000113477421.00.00.011.1333NaNSouthampton1
609609Johnson, Miss. Eleanor Ileen0.01.01.0000113477421.00.00.011.1333NaNSouthampton1
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611611Johnson, Mr. Malkolm Joackim1.00.033.0000003470621.00.00.07.7750NaNSouthampton0
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614614Johnston, Master. William Arthur 'Willie'1.00.0NaN12W./C. 66071.00.00.023.4500NaNSouthampton0
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619619Jonkoff, Mr. Lalio1.00.023.0000003492041.00.00.07.8958NaNSouthampton0
620620Jonsson, Mr. Carl1.00.032.0000003504171.00.00.07.8542NaNSouthampton1
621621Jonsson, Mr. Nils Hilding1.00.027.0000003504081.00.00.07.8542NaNSouthampton0
622622Julian, Mr. Henry Forbes1.00.050.0000001130440.00.01.026.0000E60Southampton0
623623Jussila, Miss. Katriina0.01.020.00001041361.00.00.09.8250NaNSouthampton0
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628628Kantor, Mr. Sinai1.00.034.0000102443670.01.00.026.0000NaNSouthampton0
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630630Karaic, Mr. Milan1.00.030.0000003492461.00.00.07.8958NaNSouthampton0
631631Karlsson, Mr. Einar Gervasius1.00.021.0000003500531.00.00.07.7958NaNSouthampton1
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633633Karlsson, Mr. Nils August1.00.022.0000003500601.00.00.07.5208NaNSouthampton0
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12631263Whabee, Mrs. George Joseph (Shawneene Abi-Saab)0.01.038.00000026881.00.00.07.2292NaNCherbourg1
12641264Wheadon, Mr. Edward H1.00.066.000000C.A. 245790.01.00.010.5000NaNSouthampton0
12651265Wheeler, Mr. Edwin 'Frederick'1.00.0NaN00SC/PARIS 21590.01.00.012.8750NaNSouthampton0
12661266White, Mr. Percival Wayland1.00.054.000001352810.00.01.077.2875D26Southampton0
12671267White, Mr. Richard Frasar1.00.021.000001352810.00.01.077.2875D26Southampton0
12681268White, Mrs. John Stuart (Ella Holmes)0.01.055.000000PC 177600.00.01.0135.6333C32Cherbourg1
12691269Wick, Miss. Mary Natalie0.01.031.000002369280.00.01.0164.8667C7Southampton1
12701270Wick, Mr. George Dennick1.00.057.000011369280.00.01.0164.8667NaNSouthampton0
12711271Wick, Mrs. George Dennick (Mary Hitchcock)0.01.045.000011369280.00.01.0164.8667NaNSouthampton1
12721272Widegren, Mr. Carl/Charles Peter1.00.051.0000003470641.00.00.07.7500NaNSouthampton0
12731273Widener, Mr. George Dunton1.00.050.0000111135030.00.01.0211.5000C80Cherbourg0
12741274Widener, Mr. Harry Elkins1.00.027.0000021135030.00.01.0211.5000C82Cherbourg0
12751275Widener, Mrs. George Dunton (Eleanor Elkins)0.01.050.0000111135030.00.01.0211.5000C80Cherbourg1
12761276Wiklund, Mr. Jakob Alfred1.00.018.00001031012671.00.00.06.4958NaNSouthampton0
12771277Wiklund, Mr. Karl Johan1.00.021.00001031012661.00.00.06.4958NaNSouthampton0
12781278Wilhelms, Mr. Charles1.00.031.0000002442700.01.00.013.0000NaNSouthampton1
12791279Wilkes, Mrs. James (Ellen Needs)0.01.047.0000103632721.00.00.07.0000NaNSouthampton1
12801280Willard, Miss. Constance0.01.021.0000001137950.00.01.026.5500NaNSouthampton1
12811281Willer, Mr. Aaron ('Abi Weller')1.00.0NaN0034101.00.00.08.7125NaNSouthampton0
12821282Willey, Mr. Edward1.00.0NaN00S.O./P.P. 7511.00.00.07.5500NaNSouthampton0
12831283Williams, Mr. Charles Duane1.00.051.000001PC 175970.00.01.061.3792NaNCherbourg0
12841284Williams, Mr. Charles Eugene1.00.0NaN002443730.01.00.013.0000NaNSouthampton1
12851285Williams, Mr. Howard Hugh 'Harry'1.00.0NaN00A/5 24661.00.00.08.0500NaNSouthampton0
12861286Williams, Mr. Leslie1.00.028.500000546361.00.00.016.1000NaNSouthampton0
12871287Williams, Mr. Richard Norris II1.00.021.000001PC 175970.00.01.061.3792NaNCherbourg1
12881288Williams-Lambert, Mr. Fletcher Fellows1.00.0NaN001135100.00.01.035.0000C128Southampton0
12891289Wilson, Miss. Helen Alice0.01.031.000000169660.00.01.0134.5000E39 E41Cherbourg1
12901290Windelov, Mr. Einar1.00.021.000000SOTON/OQ 31013171.00.00.07.2500NaNSouthampton0
12911291Wirz, Mr. Albert1.00.027.0000003151541.00.00.08.6625NaNSouthampton0
12921292Wiseman, Mr. Phillippe1.00.0NaN00A/4. 342441.00.00.07.2500NaNSouthampton0
12931293Wittevrongel, Mr. Camille1.00.036.0000003457711.00.00.09.5000NaNSouthampton0
12941294Woolner, Mr. Hugh1.00.0NaN00199470.00.01.035.5000C52Southampton1
12951295Wright, Miss. Marion0.01.026.0000002208440.01.00.013.5000NaNSouthampton1
12961296Wright, Mr. George1.00.062.0000001138070.00.01.026.5500NaNSouthampton0
12971297Yasbeck, Mr. Antoni1.00.027.00001026591.00.00.014.4542NaNCherbourg0
12981298Yasbeck, Mrs. Antoni (Selini Alexander)0.01.015.00001026591.00.00.014.4542NaNCherbourg1
12991299Young, Miss. Marie Grice0.01.036.000000PC 177600.00.01.0135.6333C32Cherbourg1
13001300Youseff, Mr. Gerious1.00.045.50000026281.00.00.07.2250NaNCherbourg0
13011301Yousif, Mr. Wazli1.00.0NaN0026471.00.00.07.2250NaNCherbourg0
13021302Yousseff, Mr. Gerious1.00.0NaN0026271.00.00.014.4583NaNCherbourg0
13031303Yrois, Miss. Henriette ('Mrs Harbeck')0.01.024.0000002487470.01.00.013.0000NaNSouthampton0
13041304Zabour, Miss. Hileni0.01.014.50001026651.00.00.014.4542NaNCherbourg0
13051305Zabour, Miss. Thamine0.01.0NaN1026651.00.00.014.4542NaNCherbourg0
13061306Zakarian, Mr. Mapriededer1.00.026.50000026561.00.00.07.2250NaNCherbourg0
13071307Zakarian, Mr. Ortin1.00.027.00000026701.00.00.07.2250NaNCherbourg0
13081308Zimmerman, Mr. Leo1.00.029.0000003150821.00.00.07.8750NaNSouthampton0
\n
" + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from safeds.data.tabular.transformation import OneHotEncoder\n", + "\n", + "transformed_table = OneHotEncoder().fit_and_transform(titanic, [\"sex\", \"travel_class\"])\n", + "\n", + "transformed_table\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + " 4. Transform table using `RangeScaler`, this will scale the values in the chosen `Column`s to a given range:" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 41, + "outputs": [ + { + "data": { + "text/plain": " id name sex age \\\n0 0 Abbing, Mr. Anthony male 0.524008 \n1 1 Abbott, Master. Eugene Joseph male 0.160751 \n2 2 Abbott, Mr. Rossmore Edward male 0.198330 \n3 3 Abbott, Mrs. Stanton (Rosa Hunt) female 0.436325 \n4 4 Abelseth, Miss. Karen Marie female 0.198330 \n... ... ... ... ... \n1304 1304 Zabour, Miss. Hileni female 0.179540 \n1305 1305 Zabour, Miss. Thamine female NaN \n1306 1306 Zakarian, Mr. Mapriededer male 0.329854 \n1307 1307 Zakarian, Mr. Ortin male 0.336117 \n1308 1308 Zimmerman, Mr. Leo male 0.361169 \n\n siblings_spouses parents_children ticket travel_class fare \\\n0 0 0 C.A. 5547 3 7.5500 \n1 0 2 C.A. 2673 3 20.2500 \n2 1 1 C.A. 2673 3 20.2500 \n3 1 1 C.A. 2673 3 20.2500 \n4 0 0 348125 3 7.6500 \n... ... ... ... ... ... \n1304 1 0 2665 3 14.4542 \n1305 1 0 2665 3 14.4542 \n1306 0 0 2656 3 7.2250 \n1307 0 0 2670 3 7.2250 \n1308 0 0 315082 3 7.8750 \n\n cabin port_embarked survived \n0 NaN Southampton 0 \n1 NaN Southampton 0 \n2 NaN Southampton 0 \n3 NaN Southampton 1 \n4 NaN Southampton 1 \n... ... ... ... \n1304 NaN Cherbourg 0 \n1305 NaN Cherbourg 0 \n1306 NaN Cherbourg 0 \n1307 NaN Cherbourg 0 \n1308 NaN Southampton 0 \n\n[1309 rows x 12 columns]", + "text/html": "
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
idnamesexagesiblings_spousesparents_childrentickettravel_classfarecabinport_embarkedsurvived
00Abbing, Mr. Anthonymale0.52400800C.A. 554737.5500NaNSouthampton0
11Abbott, Master. Eugene Josephmale0.16075102C.A. 2673320.2500NaNSouthampton0
22Abbott, Mr. Rossmore Edwardmale0.19833011C.A. 2673320.2500NaNSouthampton0
33Abbott, Mrs. Stanton (Rosa Hunt)female0.43632511C.A. 2673320.2500NaNSouthampton1
44Abelseth, Miss. Karen Mariefemale0.1983300034812537.6500NaNSouthampton1
55Abelseth, Mr. Olaus Jorgensenmale0.3110640034812237.6500F G63Southampton1
66Abelson, Mr. Samuelmale0.37369510P/PP 3381224.0000NaNCherbourg0
77Abelson, Mrs. Samuel (Hannah Wizosky)female0.34864310P/PP 3381224.0000NaNCherbourg1
88Abrahamsson, Mr. Abraham August Johannesmale0.24843400SOTON/O2 310128437.9250NaNSouthampton1
99Abrahim, Mrs. Joseph (Sophie Halaut Easu)female0.22338200265737.2292NaNCherbourg1
1010Adahl, Mr. Mauritz Nils Martinmale0.37369500C 707637.2500NaNSouthampton0
1111Adams, Mr. Johnmale0.3235910034182638.0500NaNSouthampton0
1212Ahlin, Mrs. Johan (Johanna Persdotter Larsson)female0.49895610754639.4750NaNSouthampton0
1313Aks, Master. Philip Frankmale0.0083500139209139.3500NaNSouthampton1
1414Aks, Mrs. Sam (Leah Rosen)female0.2233820139209139.3500NaNSouthampton1
1515Albimona, Mr. Nassef Cassemmale0.323591002699318.7875NaNCherbourg1
1616Aldworth, Mr. Charles Augustusmale0.37369500248744213.0000NaNSouthampton0
1717Alexander, Mr. Williammale0.32359100347437.8875NaNSouthampton0
1818Alhomaki, Mr. Ilmari Rudolfmale0.24843400SOTON/O2 310128737.9250NaNSouthampton0
1919Ali, Mr. Ahmedmale0.29853800SOTON/O.Q. 310131137.0500NaNSouthampton0
2020Ali, Mr. Williammale0.31106400SOTON/O.Q. 310131237.0500NaNSouthampton0
2121Allen, Miss. Elisabeth Waltonfemale0.36116900241601211.3375B5Southampton1
2222Allen, Mr. William Henrymale0.4363250037345038.0500NaNSouthampton0
2323Allison, Master. Hudson Trevormale0.009395121137811151.5500C22 C26Southampton1
2424Allison, Miss. Helen Lorainefemale0.022964121137811151.5500C22 C26Southampton0
2525Allison, Mr. Hudson Joshua Creightonmale0.373695121137811151.5500C22 C26Southampton0
2626Allison, Mrs. Hudson J C (Bessie Waldo Daniels)female0.311064121137811151.5500C22 C26Southampton0
2727Allum, Mr. Owen Georgemale0.22338200222338.3000NaNSouthampton0
2828Andersen, Mr. Albert Karvinmale0.39874700C 4001322.5250NaNSouthampton0
2929Andersen-Jensen, Miss. Carla Christine Nielsinefemale0.2359081035004637.8542NaNSouthampton1
3030Anderson, Mr. Harrymale0.5991650019952126.5500E12Southampton1
3131Andersson, Master. Sigvard Harald Eliasmale0.04801642347082331.2750NaNSouthampton0
3232Andersson, Miss. Ebba Iris Alfridafemale0.07306942347082331.2750NaNSouthampton0
3333Andersson, Miss. Ellis Anna Mariafemale0.02296442347082331.2750NaNSouthampton0
3434Andersson, Miss. Erna Alexandrafemale0.21085642310128137.9250NaNSouthampton1
3535Andersson, Miss. Ida Augusta Margaretafemale0.4739044234709137.7750NaNSouthampton0
3636Andersson, Miss. Ingeborg Constanziafemale0.11064742347082331.2750NaNSouthampton0
3737Andersson, Miss. Sigrid Elisabethfemale0.13569942347082331.2750NaNSouthampton0
3838Andersson, Mr. Anders Johanmale0.48643015347082331.2750NaNSouthampton0
3939Andersson, Mr. August Edvard ('Wennerstrom')male0.3361170035004337.7958NaNSouthampton1
4040Andersson, Mr. Johan Samuelmale0.3235910034707537.7750NaNSouthampton0
4141Andersson, Mrs. Anders Johan (Alfrida Konstant...female0.48643015347082331.2750NaNSouthampton0
4242Andreasson, Mr. Paul Edvinmale0.2484340034746637.8542NaNSouthampton0
4343Andrew, Mr. Edgardo Samuelmale0.22338200231945211.5000NaNSouthampton0
4444Andrew, Mr. Frank Thomasmale0.31106400C.A. 34050210.5000NaNSouthampton0
4545Andrews, Miss. Kornelia Theodosiafemale0.7870561013502177.9583D7Southampton1
4646Andrews, Mr. Thomas Jrmale0.4864300011205010.0000A36Southampton0
4747Angheloff, Mr. Minkomale0.3235910034920237.8958NaNSouthampton0
4848Angle, Mr. William Amale0.42379910226875226.0000NaNSouthampton0
4949Angle, Mrs. William A (Florence 'Mary' Agnes H...female0.44885210226875226.0000NaNSouthampton1
5050Appleton, Mrs. Edward Dale (Charlotte Lamson)female0.6617952011769151.4792C101Southampton1
5151Arnold-Franchi, Mr. Josefmale0.31106410349237317.8000NaNSouthampton0
5252Arnold-Franchi, Mrs. Josef (Josefine Franchi)female0.22338210349237317.8000NaNSouthampton0
5353Aronsson, Mr. Ernst Axel Algotmale0.2985380034991137.7750NaNSouthampton0
5454Artagaveytia, Mr. Ramonmale0.88726500PC 17609149.5042NaNCherbourg0
5555Ashby, Mr. Johnmale0.71190000244346213.0000NaNSouthampton0
5656Asim, Mr. Adolamale0.43632500SOTON/O.Q. 310131037.0500NaNSouthampton0
5757Asplund, Master. Carl Edgarmale0.06054242347077331.3875NaNSouthampton0
5858Asplund, Master. Clarence Gustaf Hugomale0.11064742347077331.3875NaNSouthampton0
5959Asplund, Master. Edvin Rojj Felixmale0.03549042347077331.3875NaNSouthampton1
6060Asplund, Master. Filip Oscarmale0.16075142347077331.3875NaNSouthampton0
6161Asplund, Miss. Lillian Gertrudfemale0.06054242347077331.3875NaNSouthampton1
6262Asplund, Mr. Carl Oscar Vilhelm Gustafssonmale0.49895615347077331.3875NaNSouthampton0
6363Asplund, Mr. Johan Charlesmale0.2860120035005437.7958NaNSouthampton1
6464Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...female0.47390415347077331.3875NaNSouthampton1
6565Assaf Khalil, Mrs. Mariana ('Miriam')female0.56158600269637.2250NaNCherbourg1
6666Assaf, Mr. Geriosmale0.26096000269237.2250NaNCherbourg0
6767Assam, Mr. Alimale0.28601200SOTON/O.Q. 310130937.0500NaNSouthampton0
6868Astor, Col. John Jacobmale0.58663910PC 177571227.5250C62 C64Cherbourg0
6969Astor, Mrs. John Jacob (Madeleine Talmadge Force)female0.22338210PC 177571227.5250C62 C64Cherbourg1
7070Attalah, Miss. Malakefemale0.210856002627314.4583NaNCherbourg0
7171Attalah, Mr. Sleimanmale0.37369500269437.2250NaNCherbourg0
7272Aubart, Mme. Leontine Paulinefemale0.29853800PC 17477169.3000B35Cherbourg1
7373Augustsson, Mr. Albertmale0.2860120034746837.8542NaNSouthampton0
7474Ayoub, Miss. Banourafemale0.16075100268737.2292NaNCherbourg1
7575Baccos, Mr. Raffullmale0.24843400267937.2250NaNCherbourg0
7676Backstrom, Mr. Karl Alfredmale0.398747103101278315.8500NaNSouthampton0
7777Backstrom, Mrs. Karl Alfred (Maria Mathilda Gu...female0.411273303101278315.8500NaNSouthampton1
7878Baclini, Miss. Eugeniefemale0.007306212666319.2583NaNCherbourg1
7979Baclini, Miss. Helene Barbarafemale0.007306212666319.2583NaNCherbourg1
8080Baclini, Miss. Marie Catherinefemale0.060542212666319.2583NaNCherbourg1
8181Baclini, Mrs. Solomon (Latifa Qurban)female0.298538032666319.2583NaNCherbourg1
8282Badman, Miss. Emily Louisafemale0.22338200A/4 3141638.0500NaNSouthampton1
8383Badt, Mr. Mohamedmale0.49895600262337.2250NaNCherbourg0
8484Bailey, Mr. Percy Andrewmale0.2233820029108211.5000NaNSouthampton0
8585Baimbrigge, Mr. Charles Robertmale0.28601200C.A. 31030210.5000NaNSouthampton0
8686Balkic, Mr. Cerinmale0.3235910034924837.8958NaNSouthampton0
8787Ball, Mrs. (Ada E Hall)female0.4488520028551213.0000DSouthampton1
8888Banfield, Mr. Frederick Jamesmale0.34864300C.A./SOTON 34068210.5000NaNSouthampton0
8989Barah, Mr. Hanna Assimale0.24843400266337.2292NaNCherbourg1
9090Barbara, Miss. Saiidefemale0.223382012691314.4542NaNCherbourg0
9191Barbara, Mrs. (Catherine David)female0.561586012691314.4542NaNCherbourg0
9292Barber, Miss. Ellen 'Nellie'female0.3235910019877178.8500NaNSouthampton1
9393Barkworth, Mr. Algernon Henry Wilsonmale1.0000000027042130.0000A23Southampton1
9494Barry, Miss. Juliafemale0.3361170033084437.8792NaNQueenstown0
9595Barton, Mr. David Johnmale0.2734860032466938.0500NaNSouthampton0
9696Bateman, Rev. Robert Jamesmale0.63674300S.O.P. 1166212.5250NaNSouthampton0
9797Baumann, Mr. John DmaleNaN00PC 17318125.9250NaNSouthampton0
9898Baxter, Mr. Quigg Edmondmale0.29853801PC 175581247.5208B58 B60Cherbourg0
9999Baxter, Mrs. James (Helene DeLaudeniere Chaput)female0.62421701PC 175581247.5208B58 B60Cherbourg1
100100Bazzani, Miss. Albinafemale0.3987470011813176.2917D15Cherbourg1
101101Beane, Mr. Edwardmale0.398747102908226.0000NaNSouthampton1
102102Beane, Mrs. Edward (Ethel Clarke)female0.235908102908226.0000NaNSouthampton1
103103Beattie, Mr. Thomsonmale0.4488520013050175.2417C6Cherbourg0
104104Beauchamp, Mr. Henry Jamesmale0.34864300244358226.0000NaNSouthampton0
105105Beavan, Mr. William Thomasmale0.2359080032395138.0500NaNSouthampton0
106106Becker, Master. Richard Fmale0.01043821230136239.0000F4Southampton1
107107Becker, Miss. Marion Louisefemale0.04801621230136239.0000F4Southampton1
108108Becker, Miss. Ruth Elizabethfemale0.14822521230136239.0000F4Southampton1
109109Becker, Mrs. Allen Oliver (Nellie E Baumgardner)female0.44885203230136239.0000F4Southampton1
110110Beckwith, Mr. Richard Leonardmale0.4613781111751152.5542D35Southampton1
111111Beckwith, Mrs. Richard Leonard (Sallie Monypeny)female0.5866391111751152.5542D35Southampton1
112112Beesley, Mr. Lawrencemale0.42379900248698213.0000D56Southampton1
113113Behr, Mr. Karl Howellmale0.32359100111369130.0000C148Cherbourg1
114114Bengtsson, Mr. John Viktormale0.3235910034706837.7750NaNSouthampton0
115115Bentham, Miss. Lilian Wfemale0.2359080028404213.0000NaNSouthampton1
116116Berglund, Mr. Karl Ivar Svenmale0.27348600PP 434839.3500NaNSouthampton0
117117Berriman, Mr. William Johnmale0.2860120028425213.0000NaNSouthampton0
118118Betros, Master. SemanmaleNaN00262237.2292NaNCherbourg0
119119Betros, Mr. Tannousmale0.24843400264834.0125NaNCherbourg0
120120Bidois, Miss. Rosaliefemale0.52400800PC 177571227.5250NaNCherbourg1
121121Bing, Mr. Leemale0.398747001601356.4958NaNSouthampton1
122122Bird, Miss. Ellenfemale0.36116900PC 174831221.7792C97Southampton1
123123Birkeland, Mr. Hans Martin Monsenmale0.2609600031299237.7750NaNSouthampton0
124124Birnbaum, Mr. Jakobmale0.3110640013905126.0000NaNCherbourg0
125125Bishop, Mr. Dickinson Hmale0.3110641011967191.0792B49Cherbourg1
126126Bishop, Mrs. Dickinson H (Helen Walton)female0.2359081011967191.0792B49Cherbourg1
127127Bissette, Miss. Ameliafemale0.43632500PC 177601135.6333C99Southampton1
128128Bjorklund, Mr. Ernst Herbertmale0.2233820034709037.7500NaNSouthampton0
129129Bjornstrom-Steffansson, Mr. Mauritz Hakanmale0.34864300110564126.5500C52Southampton1
130130Blackwell, Mr. Stephen Weartmale0.56158600113784135.5000TSouthampton0
131131Blank, Mr. Henrymale0.49895600112277131.0000A31Cherbourg1
132132Bonnell, Miss. Carolinefemale0.37369500369281164.8667C7Southampton1
133133Bonnell, Miss. Elizabethfemale0.72442600113783126.5500C103Southampton1
134134Borebank, Mr. John Jamesmale0.52400800110489126.5500D22Southampton0
135135Bostandyeff, Mr. Guentchomale0.3235910034922437.8958NaNSouthampton0
136136Botsford, Mr. William Hullmale0.32359100237670213.0000NaNSouthampton0
137137Boulos, Master. Akarmale0.073069112678315.2458NaNCherbourg0
138138Boulos, Miss. Nourelainfemale0.110647112678315.2458NaNCherbourg0
139139Boulos, Mr. HannamaleNaN00266437.2250NaNCherbourg0
140140Boulos, Mrs. Joseph (Sultana)femaleNaN022678315.2458NaNCherbourg0
141141Bourke, Miss. MaryfemaleNaN0236484837.7500NaNQueenstown0
142142Bourke, Mr. Johnmale0.49895611364849315.5000NaNQueenstown0
143143Bourke, Mrs. John (Catherine)female0.39874711364849315.5000NaNQueenstown0
144144Bowen, Miss. Grace Scottfemale0.56158600PC 176081262.3750NaNCherbourg1
145145Bowen, Mr. David John 'Dai'male0.2609600054636316.1000NaNSouthampton0
146146Bowenur, Mr. Solomonmale0.52400800211535213.0000NaNSouthampton0
147147Bowerman, Miss. Elsie Edithfemale0.27348601113505155.0000E33Southampton1
148148Bracken, Mr. James Hmale0.33611700220367213.0000NaNSouthampton0
149149Bradley, Miss. Bridget Deliafemale0.2734860033491437.7250NaNQueenstown1
150150Bradley, Mr. George ('George Arthur Brayton')maleNaN00111427126.5500NaNSouthampton1
151151Brady, Mr. John Bertrammale0.51148200113054130.5000A21Southampton0
152152Braf, Miss. Elin Ester Mariafemale0.2484340034747137.8542NaNSouthampton0
153153Brandeis, Mr. Emilmale0.59916500PC 17591150.4958B10Cherbourg0
154154Braund, Mr. Lewis Richardmale0.36116910346037.0458NaNSouthampton0
155155Braund, Mr. Owen Harrismale0.27348610A/5 2117137.2500NaNSouthampton0
156156Brewe, Dr. Arthur JacksonmaleNaN00112379139.6000NaNCherbourg0
157157Brobeck, Mr. Karl Rudolfmale0.2734860035004537.7958NaNSouthampton0
158158Brocklebank, Mr. William Alfredmale0.4363250036451238.0500NaNSouthampton0
159159Brown, Miss. Amelia 'Mildred'female0.29853800248733213.0000F33Southampton1
160160Brown, Miss. Edith Eileenfemale0.1858030229750239.0000NaNSouthampton1
161161Brown, Mr. Thomas William Solomonmale0.7494781129750239.0000NaNSouthampton0
162162Brown, Mrs. James Joseph (Margaret Tobin)female0.54906000PC 17610127.7208B4Cherbourg1
163163Brown, Mrs. John Murray (Caroline Lane Lamson)female0.7369522011769151.4792C101Southampton1
164164Brown, Mrs. Thomas William Solomon (Elizabeth ...female0.4989561129750239.0000NaNSouthampton1
165165Bryhl, Miss. Dagmar Jenny Ingeborgfemale0.24843410236853226.0000NaNSouthampton1
166166Bryhl, Mr. Kurt Arnold Gottfridmale0.31106410236853226.0000NaNSouthampton0
167167Buckley, Miss. Katherinefemale0.2296450032994437.2833NaNQueenstown0
168168Buckley, Mr. Danielmale0.2609600033092037.8208NaNQueenstown1
169169Bucknell, Mrs. William Robert (Emma Eliza Ward)female0.7494780011813176.2917D15Cherbourg1
170170Burke, Mr. Jeremiahmale0.2359080036522236.7500NaNQueenstown0
171171Burns, Miss. Elizabeth Margaretfemale0.51148200169661134.5000E40Cherbourg1
172172Burns, Miss. Mary Deliafemale0.2233820033096337.8792NaNQueenstown0
173173Buss, Miss. Katefemale0.4488520027849213.0000NaNSouthampton1
174174Butler, Mr. Reginald Fentonmale0.31106400234686213.0000NaNSouthampton0
175175Butt, Major. Archibald Willinghammale0.56158600113050126.5500B38Southampton0
176176Byles, Rev. Thomas Roussel Davidsmale0.52400800244310213.0000NaNSouthampton0
177177Bystrom, Mrs. (Karolina)female0.52400800236852213.0000NaNSouthampton1
178178Cacic, Miss. Mandafemale0.2609600031508738.6625NaNSouthampton0
179179Cacic, Miss. Marijafemale0.3736950031508438.6625NaNSouthampton0
180180Cacic, Mr. Jego Grgamale0.2233820031509138.6625NaNSouthampton0
181181Cacic, Mr. Lukamale0.4739040031508938.6625NaNSouthampton0
182182Cairns, Mr. AlexandermaleNaN00113798131.0000NaNSouthampton0
183183Calderhead, Mr. Edward Penningtonmale0.52400800PC 17476126.2875E24Southampton1
184184Caldwell, Master. Alden Gatesmale0.00835002248738229.0000NaNSouthampton1
185185Caldwell, Mr. Albert Francismale0.32359111248738229.0000NaNSouthampton1
186186Caldwell, Mrs. Albert Francis (Sylvia Mae Harb...female0.27348611248738229.0000NaNSouthampton1
187187Calic, Mr. Jovomale0.2108560031509338.6625NaNSouthampton0
188188Calic, Mr. Petarmale0.2108560031508638.6625NaNSouthampton0
189189Cameron, Miss. Clear Anniefemale0.43632500F.C.C. 13528221.0000NaNSouthampton1
190190Campbell, Mr. WilliammaleNaN0023985320.0000NaNSouthampton0
191191Canavan, Miss. Maryfemale0.2609600036484637.7500NaNQueenstown0
192192Canavan, Mr. Patrickmale0.2609600036485837.7500NaNQueenstown0
193193Candee, Mrs. Edward (Helen Churchill Hungerford)female0.66179500PC 17606127.4458NaNCherbourg1
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12171217van Billiard, Mr. Austin Blylermale0.50521902A/5. 851314.5000NaNSouthampton0
12181218Van der hoef, Mr. Wyckoffmale0.76200400111240133.5000B19Southampton0
12191219Van Impe, Miss. Catharinafemale0.12317302345773324.1500NaNSouthampton0
12201220Van Impe, Mr. Jean Baptistemale0.44885211345773324.1500NaNSouthampton0
12211221Van Impe, Mrs. Jean Baptiste (Rosalie Paula Go...female0.37369511345773324.1500NaNSouthampton0
12221222van Melkebeke, Mr. PhilemonmaleNaN0034577739.5000NaNSouthampton0
12231223Vande Velde, Mr. Johannes Josephmale0.4112730034578039.5000NaNSouthampton0
12241224Vande Walle, Mr. Nestor Cyrielmale0.3486430034577039.5000NaNSouthampton0
12251225Vanden Steen, Mr. Leo Petermale0.3486430034578339.5000NaNSouthampton0
12261226Vander Cruyssen, Mr. Victormale0.5866390034576539.0000NaNSouthampton0
12271227Vander Planke, Miss. Augusta Mariafemale0.22338220345764318.0000NaNSouthampton0
12281228Vander Planke, Mr. Juliusmale0.38622130345763318.0000NaNSouthampton0
12291229Vander Planke, Mr. Leo Edmondusmale0.19833020345764318.0000NaNSouthampton0
12301230Vander Planke, Mrs. Julius (Emelia Maria Vande...female0.38622110345763318.0000NaNSouthampton0
12311231Vartanian, Mr. Davidmale0.27348600265837.2250NaNCherbourg1
12321232Veal, Mr. Jamesmale0.4989560028221213.0000NaNSouthampton0
12331233Vendel, Mr. Olof Edvinmale0.2484340035041637.8542NaNSouthampton0
12341234Vestrom, Miss. Hulda Amanda Adolfinafemale0.1732770035040637.8542NaNSouthampton0
12351235Vovk, Mr. Jankomale0.2734860034925237.8958NaNSouthampton0
12361236Waelens, Mr. Achillemale0.2734860034576739.0000NaNSouthampton0
12371237Walcroft, Miss. Nelliefemale0.38622100F.C.C. 13528221.0000NaNSouthampton1
12381238Walker, Mr. William Andersonmale0.5866390036967134.0208D46Southampton0
12391239Ward, Miss. Annafemale0.43632500PC 177551512.3292NaNCherbourg1
12401240Ware, Mr. FrederickmaleNaN0035930938.0500NaNSouthampton0
12411241Ware, Mr. John Jamesmale0.37369510CA 31352221.0000NaNSouthampton0
12421242Ware, Mr. William Jefferymale0.2860121028666210.5000NaNSouthampton0
12431243Ware, Mrs. John James (Florence Louise Long)female0.38622100CA 31352221.0000NaNSouthampton1
12441244Warren, Mr. Charles WilliammaleNaN00C.A. 4986737.5500NaNSouthampton0
12451245Warren, Mr. Frank Manleymale0.79958210110813175.2500D37Cherbourg0
12461246Warren, Mrs. Frank Manley (Anna Sophia Atkinson)female0.74947810110813175.2500D37Cherbourg1
12471247Watson, Mr. Ennis HastingsmaleNaN0023985620.0000NaNSouthampton0
12481248Watt, Miss. Bertha Jfemale0.14822500C.A. 33595215.7500NaNSouthampton1
12491249Watt, Mrs. James (Elizabeth 'Bessie' Inglis Mi...female0.49895600C.A. 33595215.7500NaNSouthampton1
12501250Webber, Miss. Susanfemale0.4050100027267213.0000E101Southampton1
12511251Webber, Mr. JamesmaleNaN00SOTON/OQ 310131638.0500NaNSouthampton0
12521252Weir, Col. Johnmale0.74947800113800126.5500NaNSouthampton0
12531253Weisz, Mr. Leopoldmale0.33611710228414226.0000NaNSouthampton0
12541254Weisz, Mrs. Leopold (Mathilde Francoise Pede)female0.36116910228414226.0000NaNSouthampton1
12551255Wells, Master. Ralph Lestermale0.0229641129103223.0000NaNSouthampton1
12561256Wells, Miss. Joanfemale0.0480161129103223.0000NaNSouthampton1
12571257Wells, Mrs. Arthur Henry ('Addie' Dart Trevaskis)female0.3611690229103223.0000NaNSouthampton1
12581258Wenzel, Mr. Linhartmale0.4050100034577539.5000NaNSouthampton0
12591259West, Miss. Barbara Jfemale0.00939512C.A. 34651227.7500NaNSouthampton1
12601260West, Miss. Constance Miriumfemale0.06054212C.A. 34651227.7500NaNSouthampton1
12611261West, Mr. Edwy Arthurmale0.44885212C.A. 34651227.7500NaNSouthampton0
12621262West, Mrs. Edwy Arthur (Ada Mary Worth)female0.41127312C.A. 34651227.7500NaNSouthampton1
12631263Whabee, Mrs. George Joseph (Shawneene Abi-Saab)female0.47390400268837.2292NaNCherbourg1
12641264Wheadon, Mr. Edward Hmale0.82463500C.A. 24579210.5000NaNSouthampton0
12651265Wheeler, Mr. Edwin 'Frederick'maleNaN00SC/PARIS 2159212.8750NaNSouthampton0
12661266White, Mr. Percival Waylandmale0.6743210135281177.2875D26Southampton0
12671267White, Mr. Richard Frasarmale0.2609600135281177.2875D26Southampton0
12681268White, Mrs. John Stuart (Ella Holmes)female0.68684700PC 177601135.6333C32Cherbourg1
12691269Wick, Miss. Mary Nataliefemale0.38622102369281164.8667C7Southampton1
12701270Wick, Mr. George Dennickmale0.71190011369281164.8667NaNSouthampton0
12711271Wick, Mrs. George Dennick (Mary Hitchcock)female0.56158611369281164.8667NaNSouthampton1
12721272Widegren, Mr. Carl/Charles Petermale0.6367430034706437.7500NaNSouthampton0
12731273Widener, Mr. George Duntonmale0.624217111135031211.5000C80Cherbourg0
12741274Widener, Mr. Harry Elkinsmale0.336117021135031211.5000C82Cherbourg0
12751275Widener, Mrs. George Dunton (Eleanor Elkins)female0.624217111135031211.5000C80Cherbourg1
12761276Wiklund, Mr. Jakob Alfredmale0.22338210310126736.4958NaNSouthampton0
12771277Wiklund, Mr. Karl Johanmale0.26096010310126636.4958NaNSouthampton0
12781278Wilhelms, Mr. Charlesmale0.38622100244270213.0000NaNSouthampton1
12791279Wilkes, Mrs. James (Ellen Needs)female0.5866391036327237.0000NaNSouthampton1
12801280Willard, Miss. Constancefemale0.26096000113795126.5500NaNSouthampton1
12811281Willer, Mr. Aaron ('Abi Weller')maleNaN00341038.7125NaNSouthampton0
12821282Willey, Mr. EdwardmaleNaN00S.O./P.P. 75137.5500NaNSouthampton0
12831283Williams, Mr. Charles Duanemale0.63674301PC 17597161.3792NaNCherbourg0
12841284Williams, Mr. Charles EugenemaleNaN00244373213.0000NaNSouthampton1
12851285Williams, Mr. Howard Hugh 'Harry'maleNaN00A/5 246638.0500NaNSouthampton0
12861286Williams, Mr. Lesliemale0.3549060054636316.1000NaNSouthampton0
12871287Williams, Mr. Richard Norris IImale0.26096001PC 17597161.3792NaNCherbourg1
12881288Williams-Lambert, Mr. Fletcher FellowsmaleNaN00113510135.0000C128Southampton0
12891289Wilson, Miss. Helen Alicefemale0.38622100169661134.5000E39 E41Cherbourg1
12901290Windelov, Mr. Einarmale0.26096000SOTON/OQ 310131737.2500NaNSouthampton0
12911291Wirz, Mr. Albertmale0.3361170031515438.6625NaNSouthampton0
12921292Wiseman, Mr. PhillippemaleNaN00A/4. 3424437.2500NaNSouthampton0
12931293Wittevrongel, Mr. Camillemale0.4488520034577139.5000NaNSouthampton0
12941294Woolner, Mr. HughmaleNaN0019947135.5000C52Southampton1
12951295Wright, Miss. Marionfemale0.32359100220844213.5000NaNSouthampton1
12961296Wright, Mr. Georgemale0.77453000113807126.5500NaNSouthampton0
12971297Yasbeck, Mr. Antonimale0.336117102659314.4542NaNCherbourg0
12981298Yasbeck, Mrs. Antoni (Selini Alexander)female0.185803102659314.4542NaNCherbourg1
12991299Young, Miss. Marie Gricefemale0.44885200PC 177601135.6333C32Cherbourg1
13001300Youseff, Mr. Geriousmale0.56785000262837.2250NaNCherbourg0
13011301Yousif, Mr. WazlimaleNaN00264737.2250NaNCherbourg0
13021302Yousseff, Mr. GeriousmaleNaN002627314.4583NaNCherbourg0
13031303Yrois, Miss. Henriette ('Mrs Harbeck')female0.29853800248747213.0000NaNSouthampton0
13041304Zabour, Miss. Hilenifemale0.179540102665314.4542NaNCherbourg0
13051305Zabour, Miss. ThaminefemaleNaN102665314.4542NaNCherbourg0
13061306Zakarian, Mr. Mapriededermale0.32985400265637.2250NaNCherbourg0
13071307Zakarian, Mr. Ortinmale0.33611700267037.2250NaNCherbourg0
13081308Zimmerman, Mr. Leomale0.3611690031508237.8750NaNSouthampton0
\n
" + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from safeds.data.tabular.transformation import RangeScaler\n", + "\n", + "transformed_table = RangeScaler(0.0, 1.0).fit_and_transform(titanic, [\"age\"])\n", + "\n", + "transformed_table\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "5. Transform table using `StandardScaler`, this will standardize values of chosen `Column`s:" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 42, + "outputs": [ + { + "data": { + "text/plain": " id name sex age \\\n0 0 Abbing, Mr. Anthony male 0.841202 \n1 1 Abbott, Master. Eugene Joseph male -1.171763 \n2 2 Abbott, Mr. Rossmore Edward male -0.963526 \n3 3 Abbott, Mrs. Stanton (Rosa Hunt) female 0.355314 \n4 4 Abelseth, Miss. Karen Marie female -0.963526 \n... ... ... ... ... \n1304 1304 Zabour, Miss. Hileni female -1.067644 \n1305 1305 Zabour, Miss. Thamine female NaN \n1306 1306 Zakarian, Mr. Mapriededer male -0.234693 \n1307 1307 Zakarian, Mr. Ortin male -0.199987 \n1308 1308 Zimmerman, Mr. Leo male -0.061162 \n\n siblings_spouses parents_children ticket travel_class fare \\\n0 0 0 C.A. 5547 0.841916 7.5500 \n1 0 2 C.A. 2673 0.841916 20.2500 \n2 1 1 C.A. 2673 0.841916 20.2500 \n3 1 1 C.A. 2673 0.841916 20.2500 \n4 0 0 348125 0.841916 7.6500 \n... ... ... ... ... ... \n1304 1 0 2665 0.841916 14.4542 \n1305 1 0 2665 0.841916 14.4542 \n1306 0 0 2656 0.841916 7.2250 \n1307 0 0 2670 0.841916 7.2250 \n1308 0 0 315082 0.841916 7.8750 \n\n cabin port_embarked survived \n0 NaN Southampton 0 \n1 NaN Southampton 0 \n2 NaN Southampton 0 \n3 NaN Southampton 1 \n4 NaN Southampton 1 \n... ... ... ... \n1304 NaN Cherbourg 0 \n1305 NaN Cherbourg 0 \n1306 NaN Cherbourg 0 \n1307 NaN Cherbourg 0 \n1308 NaN Southampton 0 \n\n[1309 rows x 12 columns]", + "text/html": "
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
idnamesexagesiblings_spousesparents_childrentickettravel_classfarecabinport_embarkedsurvived
00Abbing, Mr. Anthonymale0.84120200C.A. 55470.8419167.5500NaNSouthampton0
11Abbott, Master. Eugene Josephmale-1.17176302C.A. 26730.84191620.2500NaNSouthampton0
22Abbott, Mr. Rossmore Edwardmale-0.96352611C.A. 26730.84191620.2500NaNSouthampton0
33Abbott, Mrs. Stanton (Rosa Hunt)female0.35531411C.A. 26730.84191620.2500NaNSouthampton1
44Abelseth, Miss. Karen Mariefemale-0.963526003481250.8419167.6500NaNSouthampton1
55Abelseth, Mr. Olaus Jorgensenmale-0.338812003481220.8419167.6500F G63Southampton1
66Abelson, Mr. Samuelmale0.00825110P/PP 3381-0.35209124.0000NaNCherbourg0
77Abelson, Mrs. Samuel (Hannah Wizosky)female-0.13057410P/PP 3381-0.35209124.0000NaNCherbourg1
88Abrahamsson, Mr. Abraham August Johannesmale-0.68587500SOTON/O2 31012840.8419167.9250NaNSouthampton1
99Abrahim, Mrs. Joseph (Sophie Halaut Easu)female-0.8247000026570.8419167.2292NaNCherbourg1
1010Adahl, Mr. Mauritz Nils Martinmale0.00825100C 70760.8419167.2500NaNSouthampton0
1111Adams, Mr. Johnmale-0.269400003418260.8419168.0500NaNSouthampton0
1212Ahlin, Mrs. Johan (Johanna Persdotter Larsson)female0.7023771075460.8419169.4750NaNSouthampton0
1313Aks, Master. Philip Frankmale-2.016285013920910.8419169.3500NaNSouthampton1
1414Aks, Mrs. Sam (Leah Rosen)female-0.824700013920910.8419169.3500NaNSouthampton1
1515Albimona, Mr. Nassef Cassemmale-0.2694000026990.84191618.7875NaNCherbourg1
1616Aldworth, Mr. Charles Augustusmale0.00825100248744-0.35209113.0000NaNSouthampton0
1717Alexander, Mr. Williammale-0.2694000034740.8419167.8875NaNSouthampton0
1818Alhomaki, Mr. Ilmari Rudolfmale-0.68587500SOTON/O2 31012870.8419167.9250NaNSouthampton0
1919Ali, Mr. Ahmedmale-0.40822500SOTON/O.Q. 31013110.8419167.0500NaNSouthampton0
2020Ali, Mr. Williammale-0.33881200SOTON/O.Q. 31013120.8419167.0500NaNSouthampton0
2121Allen, Miss. Elisabeth Waltonfemale-0.0611620024160-1.546098211.3375B5Southampton1
2222Allen, Mr. William Henrymale0.355314003734500.8419168.0500NaNSouthampton0
2323Allison, Master. Hudson Trevormale-2.01049612113781-1.546098151.5500C22 C26Southampton1
2424Allison, Miss. Helen Lorainefemale-1.93530212113781-1.546098151.5500C22 C26Southampton0
2525Allison, Mr. Hudson Joshua Creightonmale0.00825112113781-1.546098151.5500C22 C26Southampton0
2626Allison, Mrs. Hudson J C (Bessie Waldo Daniels)female-0.33881212113781-1.546098151.5500C22 C26Southampton0
2727Allum, Mr. Owen Georgemale-0.8247000022230.8419168.3000NaNSouthampton0
2828Andersen, Mr. Albert Karvinmale0.14707600C 40010.84191622.5250NaNSouthampton0
2929Andersen-Jensen, Miss. Carla Christine Nielsinefemale-0.755288103500460.8419167.8542NaNSouthampton1
3030Anderson, Mr. Harrymale1.2576770019952-1.54609826.5500E12Southampton1
3131Andersson, Master. Sigvard Harald Eliasmale-1.796477423470820.84191631.2750NaNSouthampton0
3232Andersson, Miss. Ebba Iris Alfridafemale-1.657651423470820.84191631.2750NaNSouthampton0
3333Andersson, Miss. Ellis Anna Mariafemale-1.935302423470820.84191631.2750NaNSouthampton0
3434Andersson, Miss. Erna Alexandrafemale-0.8941134231012810.8419167.9250NaNSouthampton1
3535Andersson, Miss. Ida Augusta Margaretafemale0.563551423470910.8419167.7750NaNSouthampton0
3636Andersson, Miss. Ingeborg Constanziafemale-1.449414423470820.84191631.2750NaNSouthampton0
3737Andersson, Miss. Sigrid Elisabethfemale-1.310588423470820.84191631.2750NaNSouthampton0
3838Andersson, Mr. Anders Johanmale0.632964153470820.84191631.2750NaNSouthampton0
3939Andersson, Mr. August Edvard ('Wennerstrom')male-0.199987003500430.8419167.7958NaNSouthampton1
4040Andersson, Mr. Johan Samuelmale-0.269400003470750.8419167.7750NaNSouthampton0
4141Andersson, Mrs. Anders Johan (Alfrida Konstant...female0.632964153470820.84191631.2750NaNSouthampton0
4242Andreasson, Mr. Paul Edvinmale-0.685875003474660.8419167.8542NaNSouthampton0
4343Andrew, Mr. Edgardo Samuelmale-0.82470000231945-0.35209111.5000NaNSouthampton0
4444Andrew, Mr. Frank Thomasmale-0.33881200C.A. 34050-0.35209110.5000NaNSouthampton0
4545Andrews, Miss. Kornelia Theodosiafemale2.2988661013502-1.54609877.9583D7Southampton1
4646Andrews, Mr. Thomas Jrmale0.63296400112050-1.5460980.0000A36Southampton0
4747Angheloff, Mr. Minkomale-0.269400003492020.8419167.8958NaNSouthampton0
4848Angle, Mr. William Amale0.28590110226875-0.35209126.0000NaNSouthampton0
4949Angle, Mrs. William A (Florence 'Mary' Agnes H...female0.42472610226875-0.35209126.0000NaNSouthampton1
5050Appleton, Mrs. Edward Dale (Charlotte Lamson)female1.6047402011769-1.54609851.4792C101Southampton1
5151Arnold-Franchi, Mr. Josefmale-0.338812103492370.84191617.8000NaNSouthampton0
5252Arnold-Franchi, Mrs. Josef (Josefine Franchi)female-0.824700103492370.84191617.8000NaNSouthampton0
5353Aronsson, Mr. Ernst Axel Algotmale-0.408225003499110.8419167.7750NaNSouthampton0
5454Artagaveytia, Mr. Ramonmale2.85416700PC 17609-1.54609849.5042NaNCherbourg0
5555Ashby, Mr. Johnmale1.88239100244346-0.35209113.0000NaNSouthampton0
5656Asim, Mr. Adolamale0.35531400SOTON/O.Q. 31013100.8419167.0500NaNSouthampton0
5757Asplund, Master. Carl Edgarmale-1.727064423470770.84191631.3875NaNSouthampton0
5858Asplund, Master. Clarence Gustaf Hugomale-1.449414423470770.84191631.3875NaNSouthampton0
5959Asplund, Master. Edvin Rojj Felixmale-1.865889423470770.84191631.3875NaNSouthampton1
6060Asplund, Master. Filip Oscarmale-1.171763423470770.84191631.3875NaNSouthampton0
6161Asplund, Miss. Lillian Gertrudfemale-1.727064423470770.84191631.3875NaNSouthampton1
6262Asplund, Mr. Carl Oscar Vilhelm Gustafssonmale0.702377153470770.84191631.3875NaNSouthampton0
6363Asplund, Mr. Johan Charlesmale-0.477637003500540.8419167.7958NaNSouthampton1
6464Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...female0.563551153470770.84191631.3875NaNSouthampton1
6565Assaf Khalil, Mrs. Mariana ('Miriam')female1.0494400026960.8419167.2250NaNCherbourg1
6666Assaf, Mr. Geriosmale-0.6164630026920.8419167.2250NaNCherbourg0
6767Assam, Mr. Alimale-0.47763700SOTON/O.Q. 31013090.8419167.0500NaNSouthampton0
6868Astor, Col. John Jacobmale1.18826510PC 17757-1.546098227.5250C62 C64Cherbourg0
6969Astor, Mrs. John Jacob (Madeleine Talmadge Force)female-0.82470010PC 17757-1.546098227.5250C62 C64Cherbourg1
7070Attalah, Miss. Malakefemale-0.8941130026270.84191614.4583NaNCherbourg0
7171Attalah, Mr. Sleimanmale0.0082510026940.8419167.2250NaNCherbourg0
7272Aubart, Mme. Leontine Paulinefemale-0.40822500PC 17477-1.54609869.3000B35Cherbourg1
7373Augustsson, Mr. Albertmale-0.477637003474680.8419167.8542NaNSouthampton0
7474Ayoub, Miss. Banourafemale-1.1717630026870.8419167.2292NaNCherbourg1
7575Baccos, Mr. Raffullmale-0.6858750026790.8419167.2250NaNCherbourg0
7676Backstrom, Mr. Karl Alfredmale0.1470761031012780.84191615.8500NaNSouthampton0
7777Backstrom, Mrs. Karl Alfred (Maria Mathilda Gu...female0.2164893031012780.84191615.8500NaNSouthampton1
7878Baclini, Miss. Eugeniefemale-2.0220682126660.84191619.2583NaNCherbourg1
7979Baclini, Miss. Helene Barbarafemale-2.0220682126660.84191619.2583NaNCherbourg1
8080Baclini, Miss. Marie Catherinefemale-1.7270642126660.84191619.2583NaNCherbourg1
8181Baclini, Mrs. Solomon (Latifa Qurban)female-0.4082250326660.84191619.2583NaNCherbourg1
8282Badman, Miss. Emily Louisafemale-0.82470000A/4 314160.8419168.0500NaNSouthampton1
8383Badt, Mr. Mohamedmale0.7023770026230.8419167.2250NaNCherbourg0
8484Bailey, Mr. Percy Andrewmale-0.8247000029108-0.35209111.5000NaNSouthampton0
8585Baimbrigge, Mr. Charles Robertmale-0.47763700C.A. 31030-0.35209110.5000NaNSouthampton0
8686Balkic, Mr. Cerinmale-0.269400003492480.8419167.8958NaNSouthampton0
8787Ball, Mrs. (Ada E Hall)female0.4247260028551-0.35209113.0000DSouthampton1
8888Banfield, Mr. Frederick Jamesmale-0.13057400C.A./SOTON 34068-0.35209110.5000NaNSouthampton0
8989Barah, Mr. Hanna Assimale-0.6858750026630.8419167.2292NaNCherbourg1
9090Barbara, Miss. Saiidefemale-0.8247000126910.84191614.4542NaNCherbourg0
9191Barbara, Mrs. (Catherine David)female1.0494400126910.84191614.4542NaNCherbourg0
9292Barber, Miss. Ellen 'Nellie'female-0.2694000019877-1.54609878.8500NaNSouthampton1
9393Barkworth, Mr. Algernon Henry Wilsonmale3.4788800027042-1.54609830.0000A23Southampton1
9494Barry, Miss. Juliafemale-0.199987003308440.8419167.8792NaNQueenstown0
9595Barton, Mr. David Johnmale-0.547050003246690.8419168.0500NaNSouthampton0
9696Bateman, Rev. Robert Jamesmale1.46591500S.O.P. 1166-0.35209112.5250NaNSouthampton0
9797Baumann, Mr. John DmaleNaN00PC 17318-1.54609825.9250NaNSouthampton0
9898Baxter, Mr. Quigg Edmondmale-0.40822501PC 17558-1.546098247.5208B58 B60Cherbourg0
9999Baxter, Mrs. James (Helene DeLaudeniere Chaput)female1.39650301PC 17558-1.546098247.5208B58 B60Cherbourg1
100100Bazzani, Miss. Albinafemale0.1470760011813-1.54609876.2917D15Cherbourg1
101101Beane, Mr. Edwardmale0.147076102908-0.35209126.0000NaNSouthampton1
102102Beane, Mrs. Edward (Ethel Clarke)female-0.755288102908-0.35209126.0000NaNSouthampton1
103103Beattie, Mr. Thomsonmale0.4247260013050-1.54609875.2417C6Cherbourg0
104104Beauchamp, Mr. Henry Jamesmale-0.13057400244358-0.35209126.0000NaNSouthampton0
105105Beavan, Mr. William Thomasmale-0.755288003239510.8419168.0500NaNSouthampton0
106106Becker, Master. Richard Fmale-2.00471421230136-0.35209139.0000F4Southampton1
107107Becker, Miss. Marion Louisefemale-1.79647721230136-0.35209139.0000F4Southampton1
108108Becker, Miss. Ruth Elizabethfemale-1.24117621230136-0.35209139.0000F4Southampton1
109109Becker, Mrs. Allen Oliver (Nellie E Baumgardner)female0.42472603230136-0.35209139.0000F4Southampton1
110110Beckwith, Mr. Richard Leonardmale0.4941391111751-1.54609852.5542D35Southampton1
111111Beckwith, Mrs. Richard Leonard (Sallie Monypeny)female1.1882651111751-1.54609852.5542D35Southampton1
112112Beesley, Mr. Lawrencemale0.28590100248698-0.35209113.0000D56Southampton1
113113Behr, Mr. Karl Howellmale-0.26940000111369-1.54609830.0000C148Cherbourg1
114114Bengtsson, Mr. John Viktormale-0.269400003470680.8419167.7750NaNSouthampton0
115115Bentham, Miss. Lilian Wfemale-0.7552880028404-0.35209113.0000NaNSouthampton1
116116Berglund, Mr. Karl Ivar Svenmale-0.54705000PP 43480.8419169.3500NaNSouthampton0
117117Berriman, Mr. William Johnmale-0.4776370028425-0.35209113.0000NaNSouthampton0
118118Betros, Master. SemanmaleNaN0026220.8419167.2292NaNCherbourg0
119119Betros, Mr. Tannousmale-0.6858750026480.8419164.0125NaNCherbourg0
120120Bidois, Miss. Rosaliefemale0.84120200PC 17757-1.546098227.5250NaNCherbourg1
121121Bing, Mr. Leemale0.1470760016010.84191656.4958NaNSouthampton1
122122Bird, Miss. Ellenfemale-0.06116200PC 17483-1.546098221.7792C97Southampton1
123123Birkeland, Mr. Hans Martin Monsenmale-0.616463003129920.8419167.7750NaNSouthampton0
124124Birnbaum, Mr. Jakobmale-0.3388120013905-1.54609826.0000NaNCherbourg0
125125Bishop, Mr. Dickinson Hmale-0.3388121011967-1.54609891.0792B49Cherbourg1
126126Bishop, Mrs. Dickinson H (Helen Walton)female-0.7552881011967-1.54609891.0792B49Cherbourg1
127127Bissette, Miss. Ameliafemale0.35531400PC 17760-1.546098135.6333C99Southampton1
128128Bjorklund, Mr. Ernst Herbertmale-0.824700003470900.8419167.7500NaNSouthampton0
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12371237Walcroft, Miss. Nelliefemale0.07766300F.C.C. 13528-0.35209121.0000NaNSouthampton1
12381238Walker, Mr. William Andersonmale1.1882650036967-1.54609834.0208D46Southampton0
12391239Ward, Miss. Annafemale0.35531400PC 17755-1.546098512.3292NaNCherbourg1
12401240Ware, Mr. FrederickmaleNaN003593090.8419168.0500NaNSouthampton0
12411241Ware, Mr. John Jamesmale0.00825110CA 31352-0.35209121.0000NaNSouthampton0
12421242Ware, Mr. William Jefferymale-0.4776371028666-0.35209110.5000NaNSouthampton0
12431243Ware, Mrs. John James (Florence Louise Long)female0.07766300CA 31352-0.35209121.0000NaNSouthampton1
12441244Warren, Mr. Charles WilliammaleNaN00C.A. 498670.8419167.5500NaNSouthampton0
12451245Warren, Mr. Frank Manleymale2.36827910110813-1.54609875.2500D37Cherbourg0
12461246Warren, Mrs. Frank Manley (Anna Sophia Atkinson)female2.09062810110813-1.54609875.2500D37Cherbourg1
12471247Watson, Mr. Ennis HastingsmaleNaN00239856-0.3520910.0000NaNSouthampton0
12481248Watt, Miss. Bertha Jfemale-1.24117600C.A. 33595-0.35209115.7500NaNSouthampton1
12491249Watt, Mrs. James (Elizabeth 'Bessie' Inglis Mi...female0.70237700C.A. 33595-0.35209115.7500NaNSouthampton1
12501250Webber, Miss. Susanfemale0.1817820027267-0.35209113.0000E101Southampton1
12511251Webber, Mr. JamesmaleNaN00SOTON/OQ 31013160.8419168.0500NaNSouthampton0
12521252Weir, Col. Johnmale2.09062800113800-1.54609826.5500NaNSouthampton0
12531253Weisz, Mr. Leopoldmale-0.19998710228414-0.35209126.0000NaNSouthampton0
12541254Weisz, Mrs. Leopold (Mathilde Francoise Pede)female-0.06116210228414-0.35209126.0000NaNSouthampton1
12551255Wells, Master. Ralph Lestermale-1.9353021129103-0.35209123.0000NaNSouthampton1
12561256Wells, Miss. Joanfemale-1.7964771129103-0.35209123.0000NaNSouthampton1
12571257Wells, Mrs. Arthur Henry ('Addie' Dart Trevaskis)female-0.0611620229103-0.35209123.0000NaNSouthampton1
12581258Wenzel, Mr. Linhartmale0.181782003457750.8419169.5000NaNSouthampton0
12591259West, Miss. Barbara Jfemale-2.01049612C.A. 34651-0.35209127.7500NaNSouthampton1
12601260West, Miss. Constance Miriumfemale-1.72706412C.A. 34651-0.35209127.7500NaNSouthampton1
12611261West, Mr. Edwy Arthurmale0.42472612C.A. 34651-0.35209127.7500NaNSouthampton0
12621262West, Mrs. Edwy Arthur (Ada Mary Worth)female0.21648912C.A. 34651-0.35209127.7500NaNSouthampton1
12631263Whabee, Mrs. George Joseph (Shawneene Abi-Saab)female0.5635510026880.8419167.2292NaNCherbourg1
12641264Wheadon, Mr. Edward Hmale2.50710400C.A. 24579-0.35209110.5000NaNSouthampton0
12651265Wheeler, Mr. Edwin 'Frederick'maleNaN00SC/PARIS 2159-0.35209112.8750NaNSouthampton0
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12691269Wick, Miss. Mary Nataliefemale0.0776630236928-1.546098164.8667C7Southampton1
12701270Wick, Mr. George Dennickmale1.8823911136928-1.546098164.8667NaNSouthampton0
12711271Wick, Mrs. George Dennick (Mary Hitchcock)female1.0494401136928-1.546098164.8667NaNSouthampton1
12721272Widegren, Mr. Carl/Charles Petermale1.465915003470640.8419167.7500NaNSouthampton0
12731273Widener, Mr. George Duntonmale1.39650311113503-1.546098211.5000C80Cherbourg0
12741274Widener, Mr. Harry Elkinsmale-0.19998702113503-1.546098211.5000C82Cherbourg0
12751275Widener, Mrs. George Dunton (Eleanor Elkins)female1.39650311113503-1.546098211.5000C80Cherbourg1
12761276Wiklund, Mr. Jakob Alfredmale-0.8247001031012670.8419166.4958NaNSouthampton0
12771277Wiklund, Mr. Karl Johanmale-0.6164631031012660.8419166.4958NaNSouthampton0
12781278Wilhelms, Mr. Charlesmale0.07766300244270-0.35209113.0000NaNSouthampton1
12791279Wilkes, Mrs. James (Ellen Needs)female1.188265103632720.8419167.0000NaNSouthampton1
12801280Willard, Miss. Constancefemale-0.61646300113795-1.54609826.5500NaNSouthampton1
12811281Willer, Mr. Aaron ('Abi Weller')maleNaN0034100.8419168.7125NaNSouthampton0
12821282Willey, Mr. EdwardmaleNaN00S.O./P.P. 7510.8419167.5500NaNSouthampton0
12831283Williams, Mr. Charles Duanemale1.46591501PC 17597-1.54609861.3792NaNCherbourg0
12841284Williams, Mr. Charles EugenemaleNaN00244373-0.35209113.0000NaNSouthampton1
12851285Williams, Mr. Howard Hugh 'Harry'maleNaN00A/5 24660.8419168.0500NaNSouthampton0
12861286Williams, Mr. Lesliemale-0.09586800546360.84191616.1000NaNSouthampton0
12871287Williams, Mr. Richard Norris IImale-0.61646301PC 17597-1.54609861.3792NaNCherbourg1
12881288Williams-Lambert, Mr. Fletcher FellowsmaleNaN00113510-1.54609835.0000C128Southampton0
12891289Wilson, Miss. Helen Alicefemale0.0776630016966-1.546098134.5000E39 E41Cherbourg1
12901290Windelov, Mr. Einarmale-0.61646300SOTON/OQ 31013170.8419167.2500NaNSouthampton0
12911291Wirz, Mr. Albertmale-0.199987003151540.8419168.6625NaNSouthampton0
12921292Wiseman, Mr. PhillippemaleNaN00A/4. 342440.8419167.2500NaNSouthampton0
12931293Wittevrongel, Mr. Camillemale0.424726003457710.8419169.5000NaNSouthampton0
12941294Woolner, Mr. HughmaleNaN0019947-1.54609835.5000C52Southampton1
12951295Wright, Miss. Marionfemale-0.26940000220844-0.35209113.5000NaNSouthampton1
12961296Wright, Mr. Georgemale2.22945400113807-1.54609826.5500NaNSouthampton0
12971297Yasbeck, Mr. Antonimale-0.1999871026590.84191614.4542NaNCherbourg0
12981298Yasbeck, Mrs. Antoni (Selini Alexander)female-1.0329381026590.84191614.4542NaNCherbourg1
12991299Young, Miss. Marie Gricefemale0.42472600PC 17760-1.546098135.6333C32Cherbourg1
13001300Youseff, Mr. Geriousmale1.0841460026280.8419167.2250NaNCherbourg0
13011301Yousif, Mr. WazlimaleNaN0026470.8419167.2250NaNCherbourg0
13021302Yousseff, Mr. GeriousmaleNaN0026270.84191614.4583NaNCherbourg0
13031303Yrois, Miss. Henriette ('Mrs Harbeck')female-0.40822500248747-0.35209113.0000NaNSouthampton0
13041304Zabour, Miss. Hilenifemale-1.0676441026650.84191614.4542NaNCherbourg0
13051305Zabour, Miss. ThaminefemaleNaN1026650.84191614.4542NaNCherbourg0
13061306Zakarian, Mr. Mapriededermale-0.2346930026560.8419167.2250NaNCherbourg0
13071307Zakarian, Mr. Ortinmale-0.1999870026700.8419167.2250NaNCherbourg0
13081308Zimmerman, Mr. Leomale-0.061162003150820.8419167.8750NaNSouthampton0
\n
" + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from safeds.data.tabular.transformation import StandardScaler\n", + "\n", + "transformed_table = StandardScaler().fit_and_transform(titanic, [\"age\", \"travel_class\"])\n", + "\n", + "transformed_table\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "# Transform column\n", + "1. Transform \"sex\" `Column` by labeling female values with 1 and male with 0:\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 43, + "outputs": [ + { + "data": { + "text/plain": " id name sex age siblings_spouses \\\n0 0 Abbing, Mr. Anthony 0 42.0 0 \n1 1 Abbott, Master. Eugene Joseph 0 13.0 0 \n2 2 Abbott, Mr. Rossmore Edward 0 16.0 1 \n3 3 Abbott, Mrs. Stanton (Rosa Hunt) 1 35.0 1 \n4 4 Abelseth, Miss. Karen Marie 1 16.0 0 \n... ... ... ... ... ... \n1304 1304 Zabour, Miss. Hileni 1 14.5 1 \n1305 1305 Zabour, Miss. Thamine 1 NaN 1 \n1306 1306 Zakarian, Mr. Mapriededer 0 26.5 0 \n1307 1307 Zakarian, Mr. Ortin 0 27.0 0 \n1308 1308 Zimmerman, Mr. Leo 0 29.0 0 \n\n parents_children ticket travel_class fare cabin port_embarked \\\n0 0 C.A. 5547 3 7.5500 NaN Southampton \n1 2 C.A. 2673 3 20.2500 NaN Southampton \n2 1 C.A. 2673 3 20.2500 NaN Southampton \n3 1 C.A. 2673 3 20.2500 NaN Southampton \n4 0 348125 3 7.6500 NaN Southampton \n... ... ... ... ... ... ... \n1304 0 2665 3 14.4542 NaN Cherbourg \n1305 0 2665 3 14.4542 NaN Cherbourg \n1306 0 2656 3 7.2250 NaN Cherbourg \n1307 0 2670 3 7.2250 NaN Cherbourg \n1308 0 315082 3 7.8750 NaN Southampton \n\n survived \n0 0 \n1 0 \n2 0 \n3 1 \n4 1 \n... ... \n1304 0 \n1305 0 \n1306 0 \n1307 0 \n1308 0 \n\n[1309 rows x 12 columns]", + "text/html": "
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
idnamesexagesiblings_spousesparents_childrentickettravel_classfarecabinport_embarkedsurvived
00Abbing, Mr. Anthony042.000000C.A. 554737.5500NaNSouthampton0
11Abbott, Master. Eugene Joseph013.000002C.A. 2673320.2500NaNSouthampton0
22Abbott, Mr. Rossmore Edward016.000011C.A. 2673320.2500NaNSouthampton0
33Abbott, Mrs. Stanton (Rosa Hunt)135.000011C.A. 2673320.2500NaNSouthampton1
44Abelseth, Miss. Karen Marie116.00000034812537.6500NaNSouthampton1
55Abelseth, Mr. Olaus Jorgensen025.00000034812237.6500F G63Southampton1
66Abelson, Mr. Samuel030.000010P/PP 3381224.0000NaNCherbourg0
77Abelson, Mrs. Samuel (Hannah Wizosky)128.000010P/PP 3381224.0000NaNCherbourg1
88Abrahamsson, Mr. Abraham August Johannes020.000000SOTON/O2 310128437.9250NaNSouthampton1
99Abrahim, Mrs. Joseph (Sophie Halaut Easu)118.000000265737.2292NaNCherbourg1
1010Adahl, Mr. Mauritz Nils Martin030.000000C 707637.2500NaNSouthampton0
1111Adams, Mr. John026.00000034182638.0500NaNSouthampton0
1212Ahlin, Mrs. Johan (Johanna Persdotter Larsson)140.000010754639.4750NaNSouthampton0
1313Aks, Master. Philip Frank00.83330139209139.3500NaNSouthampton1
1414Aks, Mrs. Sam (Leah Rosen)118.00000139209139.3500NaNSouthampton1
1515Albimona, Mr. Nassef Cassem026.0000002699318.7875NaNCherbourg1
1616Aldworth, Mr. Charles Augustus030.000000248744213.0000NaNSouthampton0
1717Alexander, Mr. William026.000000347437.8875NaNSouthampton0
1818Alhomaki, Mr. Ilmari Rudolf020.000000SOTON/O2 310128737.9250NaNSouthampton0
1919Ali, Mr. Ahmed024.000000SOTON/O.Q. 310131137.0500NaNSouthampton0
2020Ali, Mr. William025.000000SOTON/O.Q. 310131237.0500NaNSouthampton0
2121Allen, Miss. Elisabeth Walton129.000000241601211.3375B5Southampton1
2222Allen, Mr. William Henry035.00000037345038.0500NaNSouthampton0
2323Allison, Master. Hudson Trevor00.9167121137811151.5500C22 C26Southampton1
2424Allison, Miss. Helen Loraine12.0000121137811151.5500C22 C26Southampton0
2525Allison, Mr. Hudson Joshua Creighton030.0000121137811151.5500C22 C26Southampton0
2626Allison, Mrs. Hudson J C (Bessie Waldo Daniels)125.0000121137811151.5500C22 C26Southampton0
2727Allum, Mr. Owen George018.000000222338.3000NaNSouthampton0
2828Andersen, Mr. Albert Karvin032.000000C 4001322.5250NaNSouthampton0
2929Andersen-Jensen, Miss. Carla Christine Nielsine119.00001035004637.8542NaNSouthampton1
3030Anderson, Mr. Harry048.00000019952126.5500E12Southampton1
3131Andersson, Master. Sigvard Harald Elias04.000042347082331.2750NaNSouthampton0
3232Andersson, Miss. Ebba Iris Alfrida16.000042347082331.2750NaNSouthampton0
3333Andersson, Miss. Ellis Anna Maria12.000042347082331.2750NaNSouthampton0
3434Andersson, Miss. Erna Alexandra117.000042310128137.9250NaNSouthampton1
3535Andersson, Miss. Ida Augusta Margareta138.00004234709137.7750NaNSouthampton0
3636Andersson, Miss. Ingeborg Constanzia19.000042347082331.2750NaNSouthampton0
3737Andersson, Miss. Sigrid Elisabeth111.000042347082331.2750NaNSouthampton0
3838Andersson, Mr. Anders Johan039.000015347082331.2750NaNSouthampton0
3939Andersson, Mr. August Edvard ('Wennerstrom')027.00000035004337.7958NaNSouthampton1
4040Andersson, Mr. Johan Samuel026.00000034707537.7750NaNSouthampton0
4141Andersson, Mrs. Anders Johan (Alfrida Konstant...139.000015347082331.2750NaNSouthampton0
4242Andreasson, Mr. Paul Edvin020.00000034746637.8542NaNSouthampton0
4343Andrew, Mr. Edgardo Samuel018.000000231945211.5000NaNSouthampton0
4444Andrew, Mr. Frank Thomas025.000000C.A. 34050210.5000NaNSouthampton0
4545Andrews, Miss. Kornelia Theodosia163.00001013502177.9583D7Southampton1
4646Andrews, Mr. Thomas Jr039.00000011205010.0000A36Southampton0
4747Angheloff, Mr. Minko026.00000034920237.8958NaNSouthampton0
4848Angle, Mr. William A034.000010226875226.0000NaNSouthampton0
4949Angle, Mrs. William A (Florence 'Mary' Agnes H...136.000010226875226.0000NaNSouthampton1
5050Appleton, Mrs. Edward Dale (Charlotte Lamson)153.00002011769151.4792C101Southampton1
5151Arnold-Franchi, Mr. Josef025.000010349237317.8000NaNSouthampton0
5252Arnold-Franchi, Mrs. Josef (Josefine Franchi)118.000010349237317.8000NaNSouthampton0
5353Aronsson, Mr. Ernst Axel Algot024.00000034991137.7750NaNSouthampton0
5454Artagaveytia, Mr. Ramon071.000000PC 17609149.5042NaNCherbourg0
5555Ashby, Mr. John057.000000244346213.0000NaNSouthampton0
5656Asim, Mr. Adola035.000000SOTON/O.Q. 310131037.0500NaNSouthampton0
5757Asplund, Master. Carl Edgar05.000042347077331.3875NaNSouthampton0
5858Asplund, Master. Clarence Gustaf Hugo09.000042347077331.3875NaNSouthampton0
5959Asplund, Master. Edvin Rojj Felix03.000042347077331.3875NaNSouthampton1
6060Asplund, Master. Filip Oscar013.000042347077331.3875NaNSouthampton0
6161Asplund, Miss. Lillian Gertrud15.000042347077331.3875NaNSouthampton1
6262Asplund, Mr. Carl Oscar Vilhelm Gustafsson040.000015347077331.3875NaNSouthampton0
6363Asplund, Mr. Johan Charles023.00000035005437.7958NaNSouthampton1
6464Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...138.000015347077331.3875NaNSouthampton1
6565Assaf Khalil, Mrs. Mariana ('Miriam')145.000000269637.2250NaNCherbourg1
6666Assaf, Mr. Gerios021.000000269237.2250NaNCherbourg0
6767Assam, Mr. Ali023.000000SOTON/O.Q. 310130937.0500NaNSouthampton0
6868Astor, Col. John Jacob047.000010PC 177571227.5250C62 C64Cherbourg0
6969Astor, Mrs. John Jacob (Madeleine Talmadge Force)118.000010PC 177571227.5250C62 C64Cherbourg1
7070Attalah, Miss. Malake117.0000002627314.4583NaNCherbourg0
7171Attalah, Mr. Sleiman030.000000269437.2250NaNCherbourg0
7272Aubart, Mme. Leontine Pauline124.000000PC 17477169.3000B35Cherbourg1
7373Augustsson, Mr. Albert023.00000034746837.8542NaNSouthampton0
7474Ayoub, Miss. Banoura113.000000268737.2292NaNCherbourg1
7575Baccos, Mr. Raffull020.000000267937.2250NaNCherbourg0
7676Backstrom, Mr. Karl Alfred032.0000103101278315.8500NaNSouthampton0
7777Backstrom, Mrs. Karl Alfred (Maria Mathilda Gu...133.0000303101278315.8500NaNSouthampton1
7878Baclini, Miss. Eugenie10.7500212666319.2583NaNCherbourg1
7979Baclini, Miss. Helene Barbara10.7500212666319.2583NaNCherbourg1
8080Baclini, Miss. Marie Catherine15.0000212666319.2583NaNCherbourg1
8181Baclini, Mrs. Solomon (Latifa Qurban)124.0000032666319.2583NaNCherbourg1
8282Badman, Miss. Emily Louisa118.000000A/4 3141638.0500NaNSouthampton1
8383Badt, Mr. Mohamed040.000000262337.2250NaNCherbourg0
8484Bailey, Mr. Percy Andrew018.00000029108211.5000NaNSouthampton0
8585Baimbrigge, Mr. Charles Robert023.000000C.A. 31030210.5000NaNSouthampton0
8686Balkic, Mr. Cerin026.00000034924837.8958NaNSouthampton0
8787Ball, Mrs. (Ada E Hall)136.00000028551213.0000DSouthampton1
8888Banfield, Mr. Frederick James028.000000C.A./SOTON 34068210.5000NaNSouthampton0
8989Barah, Mr. Hanna Assi020.000000266337.2292NaNCherbourg1
9090Barbara, Miss. Saiide118.0000012691314.4542NaNCherbourg0
9191Barbara, Mrs. (Catherine David)145.0000012691314.4542NaNCherbourg0
9292Barber, Miss. Ellen 'Nellie'126.00000019877178.8500NaNSouthampton1
9393Barkworth, Mr. Algernon Henry Wilson080.00000027042130.0000A23Southampton1
9494Barry, Miss. Julia127.00000033084437.8792NaNQueenstown0
9595Barton, Mr. David John022.00000032466938.0500NaNSouthampton0
9696Bateman, Rev. Robert James051.000000S.O.P. 1166212.5250NaNSouthampton0
9797Baumann, Mr. John D0NaN00PC 17318125.9250NaNSouthampton0
9898Baxter, Mr. Quigg Edmond024.000001PC 175581247.5208B58 B60Cherbourg0
9999Baxter, Mrs. James (Helene DeLaudeniere Chaput)150.000001PC 175581247.5208B58 B60Cherbourg1
100100Bazzani, Miss. Albina132.00000011813176.2917D15Cherbourg1
101101Beane, Mr. Edward032.0000102908226.0000NaNSouthampton1
102102Beane, Mrs. Edward (Ethel Clarke)119.0000102908226.0000NaNSouthampton1
103103Beattie, Mr. Thomson036.00000013050175.2417C6Cherbourg0
104104Beauchamp, Mr. Henry James028.000000244358226.0000NaNSouthampton0
105105Beavan, Mr. William Thomas019.00000032395138.0500NaNSouthampton0
106106Becker, Master. Richard F01.000021230136239.0000F4Southampton1
107107Becker, Miss. Marion Louise14.000021230136239.0000F4Southampton1
108108Becker, Miss. Ruth Elizabeth112.000021230136239.0000F4Southampton1
109109Becker, Mrs. Allen Oliver (Nellie E Baumgardner)136.000003230136239.0000F4Southampton1
110110Beckwith, Mr. Richard Leonard037.00001111751152.5542D35Southampton1
111111Beckwith, Mrs. Richard Leonard (Sallie Monypeny)147.00001111751152.5542D35Southampton1
112112Beesley, Mr. Lawrence034.000000248698213.0000D56Southampton1
113113Behr, Mr. Karl Howell026.000000111369130.0000C148Cherbourg1
114114Bengtsson, Mr. John Viktor026.00000034706837.7750NaNSouthampton0
115115Bentham, Miss. Lilian W119.00000028404213.0000NaNSouthampton1
116116Berglund, Mr. Karl Ivar Sven022.000000PP 434839.3500NaNSouthampton0
117117Berriman, Mr. William John023.00000028425213.0000NaNSouthampton0
118118Betros, Master. Seman0NaN00262237.2292NaNCherbourg0
119119Betros, Mr. Tannous020.000000264834.0125NaNCherbourg0
120120Bidois, Miss. Rosalie142.000000PC 177571227.5250NaNCherbourg1
121121Bing, Mr. Lee032.0000001601356.4958NaNSouthampton1
122122Bird, Miss. Ellen129.000000PC 174831221.7792C97Southampton1
123123Birkeland, Mr. Hans Martin Monsen021.00000031299237.7750NaNSouthampton0
124124Birnbaum, Mr. Jakob025.00000013905126.0000NaNCherbourg0
125125Bishop, Mr. Dickinson H025.00001011967191.0792B49Cherbourg1
126126Bishop, Mrs. Dickinson H (Helen Walton)119.00001011967191.0792B49Cherbourg1
127127Bissette, Miss. Amelia135.000000PC 177601135.6333C99Southampton1
128128Bjorklund, Mr. Ernst Herbert018.00000034709037.7500NaNSouthampton0
129129Bjornstrom-Steffansson, Mr. Mauritz Hakan028.000000110564126.5500C52Southampton1
130130Blackwell, Mr. Stephen Weart045.000000113784135.5000TSouthampton0
131131Blank, Mr. Henry040.000000112277131.0000A31Cherbourg1
132132Bonnell, Miss. Caroline130.000000369281164.8667C7Southampton1
133133Bonnell, Miss. Elizabeth158.000000113783126.5500C103Southampton1
134134Borebank, Mr. John James042.000000110489126.5500D22Southampton0
135135Bostandyeff, Mr. Guentcho026.00000034922437.8958NaNSouthampton0
136136Botsford, Mr. William Hull026.000000237670213.0000NaNSouthampton0
137137Boulos, Master. Akar06.0000112678315.2458NaNCherbourg0
138138Boulos, Miss. Nourelain19.0000112678315.2458NaNCherbourg0
139139Boulos, Mr. Hanna0NaN00266437.2250NaNCherbourg0
140140Boulos, Mrs. Joseph (Sultana)1NaN022678315.2458NaNCherbourg0
141141Bourke, Miss. Mary1NaN0236484837.7500NaNQueenstown0
142142Bourke, Mr. John040.000011364849315.5000NaNQueenstown0
143143Bourke, Mrs. John (Catherine)132.000011364849315.5000NaNQueenstown0
144144Bowen, Miss. Grace Scott145.000000PC 176081262.3750NaNCherbourg1
145145Bowen, Mr. David John 'Dai'021.00000054636316.1000NaNSouthampton0
146146Bowenur, Mr. Solomon042.000000211535213.0000NaNSouthampton0
147147Bowerman, Miss. Elsie Edith122.000001113505155.0000E33Southampton1
148148Bracken, Mr. James H027.000000220367213.0000NaNSouthampton0
149149Bradley, Miss. Bridget Delia122.00000033491437.7250NaNQueenstown1
150150Bradley, Mr. George ('George Arthur Brayton')0NaN00111427126.5500NaNSouthampton1
151151Brady, Mr. John Bertram041.000000113054130.5000A21Southampton0
152152Braf, Miss. Elin Ester Maria120.00000034747137.8542NaNSouthampton0
153153Brandeis, Mr. Emil048.000000PC 17591150.4958B10Cherbourg0
154154Braund, Mr. Lewis Richard029.000010346037.0458NaNSouthampton0
155155Braund, Mr. Owen Harris022.000010A/5 2117137.2500NaNSouthampton0
156156Brewe, Dr. Arthur Jackson0NaN00112379139.6000NaNCherbourg0
157157Brobeck, Mr. Karl Rudolf022.00000035004537.7958NaNSouthampton0
158158Brocklebank, Mr. William Alfred035.00000036451238.0500NaNSouthampton0
159159Brown, Miss. Amelia 'Mildred'124.000000248733213.0000F33Southampton1
160160Brown, Miss. Edith Eileen115.00000229750239.0000NaNSouthampton1
161161Brown, Mr. Thomas William Solomon060.00001129750239.0000NaNSouthampton0
162162Brown, Mrs. James Joseph (Margaret Tobin)144.000000PC 17610127.7208B4Cherbourg1
163163Brown, Mrs. John Murray (Caroline Lane Lamson)159.00002011769151.4792C101Southampton1
164164Brown, Mrs. Thomas William Solomon (Elizabeth ...140.00001129750239.0000NaNSouthampton1
165165Bryhl, Miss. Dagmar Jenny Ingeborg120.000010236853226.0000NaNSouthampton1
166166Bryhl, Mr. Kurt Arnold Gottfrid025.000010236853226.0000NaNSouthampton0
167167Buckley, Miss. Katherine118.50000032994437.2833NaNQueenstown0
168168Buckley, Mr. Daniel021.00000033092037.8208NaNQueenstown1
169169Bucknell, Mrs. William Robert (Emma Eliza Ward)160.00000011813176.2917D15Cherbourg1
170170Burke, Mr. Jeremiah019.00000036522236.7500NaNQueenstown0
171171Burns, Miss. Elizabeth Margaret141.000000169661134.5000E40Cherbourg1
172172Burns, Miss. Mary Delia118.00000033096337.8792NaNQueenstown0
173173Buss, Miss. Kate136.00000027849213.0000NaNSouthampton1
174174Butler, Mr. Reginald Fenton025.000000234686213.0000NaNSouthampton0
175175Butt, Major. Archibald Willingham045.000000113050126.5500B38Southampton0
176176Byles, Rev. Thomas Roussel Davids042.000000244310213.0000NaNSouthampton0
177177Bystrom, Mrs. (Karolina)142.000000236852213.0000NaNSouthampton1
178178Cacic, Miss. Manda121.00000031508738.6625NaNSouthampton0
179179Cacic, Miss. Marija130.00000031508438.6625NaNSouthampton0
180180Cacic, Mr. Jego Grga018.00000031509138.6625NaNSouthampton0
181181Cacic, Mr. Luka038.00000031508938.6625NaNSouthampton0
182182Cairns, Mr. Alexander0NaN00113798131.0000NaNSouthampton0
183183Calderhead, Mr. Edward Pennington042.000000PC 17476126.2875E24Southampton1
184184Caldwell, Master. Alden Gates00.833302248738229.0000NaNSouthampton1
185185Caldwell, Mr. Albert Francis026.000011248738229.0000NaNSouthampton1
186186Caldwell, Mrs. Albert Francis (Sylvia Mae Harb...122.000011248738229.0000NaNSouthampton1
187187Calic, Mr. Jovo017.00000031509338.6625NaNSouthampton0
188188Calic, Mr. Petar017.00000031508638.6625NaNSouthampton0
189189Cameron, Miss. Clear Annie135.000000F.C.C. 13528221.0000NaNSouthampton1
190190Campbell, Mr. William0NaN0023985320.0000NaNSouthampton0
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217217Cavendish, Mrs. Tyrell William (Julia Florence...176.00001019877178.8500C46Southampton1
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222222Chambers, Mrs. Norman Campbell (Bertha Griggs)133.000010113806153.1000E8Southampton1
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253253Collyer, Miss. Marjorie 'Lottie'18.000002C.A. 31921226.2500NaNSouthampton1
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283283Crosby, Miss. Harriet R136.000002WE/P 5735171.0000B22Southampton1
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285285Culumovic, Mr. Jeso017.00000031509038.6625NaNSouthampton0
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297297Danbom, Mr. Ernst Gilbert034.000011347080314.4000NaNSouthampton0
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302302Dantcheff, Mr. Ristiu025.00000034920337.8958NaNSouthampton0
303303Davidson, Mr. Thornton031.000010F.C. 12750152.0000B71Southampton0
304304Davidson, Mrs. Thornton (Orian Hays)127.000012F.C. 12750152.0000B71Southampton1
305305Davies, Master. John Morgan Jr08.000011C.A. 33112236.7500NaNSouthampton1
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321321Dean, Master. Bertram Vere01.000012C.A. 2315320.5750NaNSouthampton1
322322Dean, Miss. Elizabeth Gladys 'Millvina'10.166712C.A. 2315320.5750NaNSouthampton1
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335335Dick, Mr. Albert Adrian031.00001017474157.0000B20Southampton1
336336Dick, Mrs. Albert Adrian (Vera Gillespie)117.00001017474157.0000B20Southampton1
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340340Dodge, Dr. Washington053.00001133638181.8583A34Southampton1
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358358Drew, Mrs. James Vivian (Lulu Thorne Christian)134.00001128220232.5000NaNSouthampton1
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360360Duff Gordon, Lady. (Lucille Christiana Sutherl...148.00001011755139.6000A16Cherbourg1
361361Duff Gordon, Sir. Cosmo Edmund ('Mr Morgan')049.000010PC 17485156.9292A20Cherbourg1
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368368Earnshaw, Mrs. Boulton (Olive Potter)123.00000111767183.1583C54Cherbourg1
369369Edvardsson, Mr. Gustaf Hjalmar018.00000034991237.7750NaNSouthampton0
370370Eitemiller, Mr. George Floyd023.00000029751213.0000NaNSouthampton0
371371Eklund, Mr. Hans Linus016.00000034707437.7750NaNSouthampton0
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385385Fahlstrom, Mr. Arne Jonas018.000000236171213.0000NaNSouthampton0
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399399Foley, Mr. Joseph026.00000033091037.8792NaNQueenstown0
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408408Foreman, Mr. Benjamin Laventall030.000000113051127.7500C111Cherbourg0
409409Fortune, Miss. Alice Elizabeth124.000032199501263.0000C23 C25 C27Southampton1
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421421Frauenthal, Mr. Isaac Gerald043.00001017765127.7208D40Cherbourg1
422422Frauenthal, Mrs. Henry William (Clara Heinshei...1NaN10PC 176111133.6500NaNSouthampton1
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424424Frolicher-Stehli, Mr. Maxmillian060.00001113567179.2000B41Cherbourg1
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430430Futrelle, Mrs. Jacques Heath (Lily May Peel)135.000010113803153.1000C123Southampton1
431431Fynney, Mr. Joseph J035.000000239865226.0000NaNSouthampton0
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455455Goldenberg, Mr. Samuel L049.00001017453189.1042C92Cherbourg1
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461461Goldsmith, Mrs. Frank John (Emily Alice Brown)131.000011363291320.5250NaNSouthampton1
462462Goncalves, Mr. Manuel Estanslas038.000000SOTON/O.Q. 310130637.0500NaNSouthampton0
463463Goodwin, Master. Harold Victor09.000052CA 2144346.9000NaNSouthampton0
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465465Goodwin, Master. William Frederick011.000052CA 2144346.9000NaNSouthampton0
466466Goodwin, Miss. Jessie Allis110.000052CA 2144346.9000NaNSouthampton0
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12941294Woolner, Mr. Hugh0NaN0019947135.5000C52Southampton1
12951295Wright, Miss. Marion126.000000220844213.5000NaNSouthampton1
12961296Wright, Mr. George062.000000113807126.5500NaNSouthampton0
12971297Yasbeck, Mr. Antoni027.0000102659314.4542NaNCherbourg0
12981298Yasbeck, Mrs. Antoni (Selini Alexander)115.0000102659314.4542NaNCherbourg1
12991299Young, Miss. Marie Grice136.000000PC 177601135.6333C32Cherbourg1
13001300Youseff, Mr. Gerious045.500000262837.2250NaNCherbourg0
13011301Yousif, Mr. Wazli0NaN00264737.2250NaNCherbourg0
13021302Yousseff, Mr. Gerious0NaN002627314.4583NaNCherbourg0
13031303Yrois, Miss. Henriette ('Mrs Harbeck')124.000000248747213.0000NaNSouthampton0
13041304Zabour, Miss. Hileni114.5000102665314.4542NaNCherbourg0
13051305Zabour, Miss. Thamine1NaN102665314.4542NaNCherbourg0
13061306Zakarian, Mr. Mapriededer026.500000265637.2250NaNCherbourg0
13071307Zakarian, Mr. Ortin027.000000267037.2250NaNCherbourg0
13081308Zimmerman, Mr. Leo029.00000031508237.8750NaNSouthampton0
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" + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic.transform_column(\"sex\", lambda row: 1 if row.get_value(\"sex\") == \"female\" else 0)\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "2. Transform values of \"parents_children\" `Column` into yes or no, depending on whether passenger has children:\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 44, + "outputs": [ + { + "data": { + "text/plain": " id name sex age siblings_spouses \\\n0 0 Abbing, Mr. Anthony male 42.0 0 \n1 1 Abbott, Master. Eugene Joseph male 13.0 0 \n2 2 Abbott, Mr. Rossmore Edward male 16.0 1 \n3 3 Abbott, Mrs. Stanton (Rosa Hunt) female 35.0 1 \n4 4 Abelseth, Miss. Karen Marie female 16.0 0 \n... ... ... ... ... ... \n1304 1304 Zabour, Miss. Hileni female 14.5 1 \n1305 1305 Zabour, Miss. Thamine female NaN 1 \n1306 1306 Zakarian, Mr. Mapriededer male 26.5 0 \n1307 1307 Zakarian, Mr. Ortin male 27.0 0 \n1308 1308 Zimmerman, Mr. Leo male 29.0 0 \n\n parents_children ticket travel_class fare cabin port_embarked \\\n0 No C.A. 5547 3 7.5500 NaN Southampton \n1 Yes C.A. 2673 3 20.2500 NaN Southampton \n2 Yes C.A. 2673 3 20.2500 NaN Southampton \n3 Yes C.A. 2673 3 20.2500 NaN Southampton \n4 No 348125 3 7.6500 NaN Southampton \n... ... ... ... ... ... ... \n1304 No 2665 3 14.4542 NaN Cherbourg \n1305 No 2665 3 14.4542 NaN Cherbourg \n1306 No 2656 3 7.2250 NaN Cherbourg \n1307 No 2670 3 7.2250 NaN Cherbourg \n1308 No 315082 3 7.8750 NaN Southampton \n\n survived \n0 0 \n1 0 \n2 0 \n3 1 \n4 1 \n... ... \n1304 0 \n1305 0 \n1306 0 \n1307 0 \n1308 0 \n\n[1309 rows x 12 columns]", + "text/html": "
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
idnamesexagesiblings_spousesparents_childrentickettravel_classfarecabinport_embarkedsurvived
00Abbing, Mr. Anthonymale42.00000NoC.A. 554737.5500NaNSouthampton0
11Abbott, Master. Eugene Josephmale13.00000YesC.A. 2673320.2500NaNSouthampton0
22Abbott, Mr. Rossmore Edwardmale16.00001YesC.A. 2673320.2500NaNSouthampton0
33Abbott, Mrs. Stanton (Rosa Hunt)female35.00001YesC.A. 2673320.2500NaNSouthampton1
44Abelseth, Miss. Karen Mariefemale16.00000No34812537.6500NaNSouthampton1
55Abelseth, Mr. Olaus Jorgensenmale25.00000No34812237.6500F G63Southampton1
66Abelson, Mr. Samuelmale30.00001NoP/PP 3381224.0000NaNCherbourg0
77Abelson, Mrs. Samuel (Hannah Wizosky)female28.00001NoP/PP 3381224.0000NaNCherbourg1
88Abrahamsson, Mr. Abraham August Johannesmale20.00000NoSOTON/O2 310128437.9250NaNSouthampton1
99Abrahim, Mrs. Joseph (Sophie Halaut Easu)female18.00000No265737.2292NaNCherbourg1
1010Adahl, Mr. Mauritz Nils Martinmale30.00000NoC 707637.2500NaNSouthampton0
1111Adams, Mr. Johnmale26.00000No34182638.0500NaNSouthampton0
1212Ahlin, Mrs. Johan (Johanna Persdotter Larsson)female40.00001No754639.4750NaNSouthampton0
1313Aks, Master. Philip Frankmale0.83330Yes39209139.3500NaNSouthampton1
1414Aks, Mrs. Sam (Leah Rosen)female18.00000Yes39209139.3500NaNSouthampton1
1515Albimona, Mr. Nassef Cassemmale26.00000No2699318.7875NaNCherbourg1
1616Aldworth, Mr. Charles Augustusmale30.00000No248744213.0000NaNSouthampton0
1717Alexander, Mr. Williammale26.00000No347437.8875NaNSouthampton0
1818Alhomaki, Mr. Ilmari Rudolfmale20.00000NoSOTON/O2 310128737.9250NaNSouthampton0
1919Ali, Mr. Ahmedmale24.00000NoSOTON/O.Q. 310131137.0500NaNSouthampton0
2020Ali, Mr. Williammale25.00000NoSOTON/O.Q. 310131237.0500NaNSouthampton0
2121Allen, Miss. Elisabeth Waltonfemale29.00000No241601211.3375B5Southampton1
2222Allen, Mr. William Henrymale35.00000No37345038.0500NaNSouthampton0
2323Allison, Master. Hudson Trevormale0.91671Yes1137811151.5500C22 C26Southampton1
2424Allison, Miss. Helen Lorainefemale2.00001Yes1137811151.5500C22 C26Southampton0
2525Allison, Mr. Hudson Joshua Creightonmale30.00001Yes1137811151.5500C22 C26Southampton0
2626Allison, Mrs. Hudson J C (Bessie Waldo Daniels)female25.00001Yes1137811151.5500C22 C26Southampton0
2727Allum, Mr. Owen Georgemale18.00000No222338.3000NaNSouthampton0
2828Andersen, Mr. Albert Karvinmale32.00000NoC 4001322.5250NaNSouthampton0
2929Andersen-Jensen, Miss. Carla Christine Nielsinefemale19.00001No35004637.8542NaNSouthampton1
3030Anderson, Mr. Harrymale48.00000No19952126.5500E12Southampton1
3131Andersson, Master. Sigvard Harald Eliasmale4.00004Yes347082331.2750NaNSouthampton0
3232Andersson, Miss. Ebba Iris Alfridafemale6.00004Yes347082331.2750NaNSouthampton0
3333Andersson, Miss. Ellis Anna Mariafemale2.00004Yes347082331.2750NaNSouthampton0
3434Andersson, Miss. Erna Alexandrafemale17.00004Yes310128137.9250NaNSouthampton1
3535Andersson, Miss. Ida Augusta Margaretafemale38.00004Yes34709137.7750NaNSouthampton0
3636Andersson, Miss. Ingeborg Constanziafemale9.00004Yes347082331.2750NaNSouthampton0
3737Andersson, Miss. Sigrid Elisabethfemale11.00004Yes347082331.2750NaNSouthampton0
3838Andersson, Mr. Anders Johanmale39.00001Yes347082331.2750NaNSouthampton0
3939Andersson, Mr. August Edvard ('Wennerstrom')male27.00000No35004337.7958NaNSouthampton1
4040Andersson, Mr. Johan Samuelmale26.00000No34707537.7750NaNSouthampton0
4141Andersson, Mrs. Anders Johan (Alfrida Konstant...female39.00001Yes347082331.2750NaNSouthampton0
4242Andreasson, Mr. Paul Edvinmale20.00000No34746637.8542NaNSouthampton0
4343Andrew, Mr. Edgardo Samuelmale18.00000No231945211.5000NaNSouthampton0
4444Andrew, Mr. Frank Thomasmale25.00000NoC.A. 34050210.5000NaNSouthampton0
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4646Andrews, Mr. Thomas Jrmale39.00000No11205010.0000A36Southampton0
4747Angheloff, Mr. Minkomale26.00000No34920237.8958NaNSouthampton0
4848Angle, Mr. William Amale34.00001No226875226.0000NaNSouthampton0
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5151Arnold-Franchi, Mr. Josefmale25.00001No349237317.8000NaNSouthampton0
5252Arnold-Franchi, Mrs. Josef (Josefine Franchi)female18.00001No349237317.8000NaNSouthampton0
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5555Ashby, Mr. Johnmale57.00000No244346213.0000NaNSouthampton0
5656Asim, Mr. Adolamale35.00000NoSOTON/O.Q. 310131037.0500NaNSouthampton0
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5858Asplund, Master. Clarence Gustaf Hugomale9.00004Yes347077331.3875NaNSouthampton0
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6363Asplund, Mr. Johan Charlesmale23.00000No35005437.7958NaNSouthampton1
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9999Baxter, Mrs. James (Helene DeLaudeniere Chaput)female50.00000YesPC 175581247.5208B58 B60Cherbourg1
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102102Beane, Mrs. Edward (Ethel Clarke)female19.00001No2908226.0000NaNSouthampton1
103103Beattie, Mr. Thomsonmale36.00000No13050175.2417C6Cherbourg0
104104Beauchamp, Mr. Henry Jamesmale28.00000No244358226.0000NaNSouthampton0
105105Beavan, Mr. William Thomasmale19.00000No32395138.0500NaNSouthampton0
106106Becker, Master. Richard Fmale1.00002Yes230136239.0000F4Southampton1
107107Becker, Miss. Marion Louisefemale4.00002Yes230136239.0000F4Southampton1
108108Becker, Miss. Ruth Elizabethfemale12.00002Yes230136239.0000F4Southampton1
109109Becker, Mrs. Allen Oliver (Nellie E Baumgardner)female36.00000Yes230136239.0000F4Southampton1
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111111Beckwith, Mrs. Richard Leonard (Sallie Monypeny)female47.00001Yes11751152.5542D35Southampton1
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115115Bentham, Miss. Lilian Wfemale19.00000No28404213.0000NaNSouthampton1
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117117Berriman, Mr. William Johnmale23.00000No28425213.0000NaNSouthampton0
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122122Bird, Miss. Ellenfemale29.00000NoPC 174831221.7792C97Southampton1
123123Birkeland, Mr. Hans Martin Monsenmale21.00000No31299237.7750NaNSouthampton0
124124Birnbaum, Mr. Jakobmale25.00000No13905126.0000NaNCherbourg0
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127127Bissette, Miss. Ameliafemale35.00000NoPC 177601135.6333C99Southampton1
128128Bjorklund, Mr. Ernst Herbertmale18.00000No34709037.7500NaNSouthampton0
129129Bjornstrom-Steffansson, Mr. Mauritz Hakanmale28.00000No110564126.5500C52Southampton1
130130Blackwell, Mr. Stephen Weartmale45.00000No113784135.5000TSouthampton0
131131Blank, Mr. Henrymale40.00000No112277131.0000A31Cherbourg1
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133133Bonnell, Miss. Elizabethfemale58.00000No113783126.5500C103Southampton1
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135135Bostandyeff, Mr. Guentchomale26.00000No34922437.8958NaNSouthampton0
136136Botsford, Mr. William Hullmale26.00000No237670213.0000NaNSouthampton0
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139139Boulos, Mr. HannamaleNaN0No266437.2250NaNCherbourg0
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144144Bowen, Miss. Grace Scottfemale45.00000NoPC 176081262.3750NaNCherbourg1
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146146Bowenur, Mr. Solomonmale42.00000No211535213.0000NaNSouthampton0
147147Bowerman, Miss. Elsie Edithfemale22.00000Yes113505155.0000E33Southampton1
148148Bracken, Mr. James Hmale27.00000No220367213.0000NaNSouthampton0
149149Bradley, Miss. Bridget Deliafemale22.00000No33491437.7250NaNQueenstown1
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151151Brady, Mr. John Bertrammale41.00000No113054130.5000A21Southampton0
152152Braf, Miss. Elin Ester Mariafemale20.00000No34747137.8542NaNSouthampton0
153153Brandeis, Mr. Emilmale48.00000NoPC 17591150.4958B10Cherbourg0
154154Braund, Mr. Lewis Richardmale29.00001No346037.0458NaNSouthampton0
155155Braund, Mr. Owen Harrismale22.00001NoA/5 2117137.2500NaNSouthampton0
156156Brewe, Dr. Arthur JacksonmaleNaN0No112379139.6000NaNCherbourg0
157157Brobeck, Mr. Karl Rudolfmale22.00000No35004537.7958NaNSouthampton0
158158Brocklebank, Mr. William Alfredmale35.00000No36451238.0500NaNSouthampton0
159159Brown, Miss. Amelia 'Mildred'female24.00000No248733213.0000F33Southampton1
160160Brown, Miss. Edith Eileenfemale15.00000Yes29750239.0000NaNSouthampton1
161161Brown, Mr. Thomas William Solomonmale60.00001Yes29750239.0000NaNSouthampton0
162162Brown, Mrs. James Joseph (Margaret Tobin)female44.00000NoPC 17610127.7208B4Cherbourg1
163163Brown, Mrs. John Murray (Caroline Lane Lamson)female59.00002No11769151.4792C101Southampton1
164164Brown, Mrs. Thomas William Solomon (Elizabeth ...female40.00001Yes29750239.0000NaNSouthampton1
165165Bryhl, Miss. Dagmar Jenny Ingeborgfemale20.00001No236853226.0000NaNSouthampton1
166166Bryhl, Mr. Kurt Arnold Gottfridmale25.00001No236853226.0000NaNSouthampton0
167167Buckley, Miss. Katherinefemale18.50000No32994437.2833NaNQueenstown0
168168Buckley, Mr. Danielmale21.00000No33092037.8208NaNQueenstown1
169169Bucknell, Mrs. William Robert (Emma Eliza Ward)female60.00000No11813176.2917D15Cherbourg1
170170Burke, Mr. Jeremiahmale19.00000No36522236.7500NaNQueenstown0
171171Burns, Miss. Elizabeth Margaretfemale41.00000No169661134.5000E40Cherbourg1
172172Burns, Miss. Mary Deliafemale18.00000No33096337.8792NaNQueenstown0
173173Buss, Miss. Katefemale36.00000No27849213.0000NaNSouthampton1
174174Butler, Mr. Reginald Fentonmale25.00000No234686213.0000NaNSouthampton0
175175Butt, Major. Archibald Willinghammale45.00000No113050126.5500B38Southampton0
176176Byles, Rev. Thomas Roussel Davidsmale42.00000No244310213.0000NaNSouthampton0
177177Bystrom, Mrs. (Karolina)female42.00000No236852213.0000NaNSouthampton1
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179179Cacic, Miss. Marijafemale30.00000No31508438.6625NaNSouthampton0
180180Cacic, Mr. Jego Grgamale18.00000No31509138.6625NaNSouthampton0
181181Cacic, Mr. Lukamale38.00000No31508938.6625NaNSouthampton0
182182Cairns, Mr. AlexandermaleNaN0No113798131.0000NaNSouthampton0
183183Calderhead, Mr. Edward Penningtonmale42.00000NoPC 17476126.2875E24Southampton1
184184Caldwell, Master. Alden Gatesmale0.83330Yes248738229.0000NaNSouthampton1
185185Caldwell, Mr. Albert Francismale26.00001Yes248738229.0000NaNSouthampton1
186186Caldwell, Mrs. Albert Francis (Sylvia Mae Harb...female22.00001Yes248738229.0000NaNSouthampton1
187187Calic, Mr. Jovomale17.00000No31509338.6625NaNSouthampton0
188188Calic, Mr. Petarmale17.00000No31508638.6625NaNSouthampton0
189189Cameron, Miss. Clear Anniefemale35.00000NoF.C.C. 13528221.0000NaNSouthampton1
190190Campbell, Mr. WilliammaleNaN0No23985320.0000NaNSouthampton0
191191Canavan, Miss. Maryfemale21.00000No36484637.7500NaNQueenstown0
192192Canavan, Mr. Patrickmale21.00000No36485837.7500NaNQueenstown0
193193Candee, Mrs. Edward (Helen Churchill Hungerford)female53.00000NoPC 17606127.4458NaNCherbourg1
194194Cann, Mr. Ernest Charlesmale21.00000NoA./5. 215238.0500NaNSouthampton0
195195Caram, Mr. JosephmaleNaN1No2689314.4583NaNCherbourg0
196196Caram, Mrs. Joseph (Maria Elias)femaleNaN1No2689314.4583NaNCherbourg0
197197Carbines, Mr. Williammale19.00000No28424213.0000NaNSouthampton0
198198Cardeza, Mr. Thomas Drake Martinezmale36.00000YesPC 177551512.3292B51 B53 B55Cherbourg1
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200200Carlsson, Mr. August Sigfridmale28.00000No35004237.7958NaNSouthampton0
201201Carlsson, Mr. Carl Robertmale24.00000No35040937.8542NaNSouthampton0
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203203Carr, Miss. Helen 'Ellen'female16.00000No36723137.7500NaNQueenstown1
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206206Carrau, Mr. Jose Pedromale17.00000No113059147.1000NaNSouthampton0
207207Carter, Master. William Thornton IImale11.00001Yes1137601120.0000B96 B98Southampton1
208208Carter, Miss. Lucile Polkfemale14.00001Yes1137601120.0000B96 B98Southampton1
209209Carter, Mr. William Ernestmale36.00001Yes1137601120.0000B96 B98Southampton1
210210Carter, Mrs. Ernest Courtenay (Lilian Hughes)female44.00001No244252226.0000NaNSouthampton0
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212212Carter, Rev. Ernest Courtenaymale54.00001No244252226.0000NaNSouthampton0
213213Carver, Mr. Alfred Johnmale28.00000No39209537.2500NaNSouthampton0
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217217Cavendish, Mrs. Tyrell William (Julia Florence...female76.00001No19877178.8500C46Southampton1
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219219Chaffee, Mr. Herbert Fullermale46.00001NoW.E.P. 5734161.1750E31Southampton0
220220Chaffee, Mrs. Herbert Fuller (Carrie Constance...female47.00001NoW.E.P. 5734161.1750E31Southampton1
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222222Chambers, Mrs. Norman Campbell (Bertha Griggs)female33.00001No113806153.1000E8Southampton1
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228228Cherry, Miss. Gladysfemale30.00000No110152186.5000B77Southampton1
229229Chevre, Mr. Paul Romainemale45.00000NoPC 17594129.7000A9Cherbourg1
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231231Chip, Mr. Changmale32.00000No1601356.4958NaNSouthampton1
232232Chisholm, Mr. Roderick Robert CrispinmaleNaN0No11205110.0000NaNSouthampton0
233233Christmann, Mr. Emilmale29.00000No34327638.0500NaNSouthampton0
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235235Christy, Mrs. (Alice Frances)female45.00000Yes237789230.0000NaNSouthampton1
236236Chronopoulos, Mr. Apostolosmale26.00001No2680314.4542NaNCherbourg0
237237Chronopoulos, Mr. Demetriosmale18.00001No2680314.4542NaNCherbourg0
238238Clark, Mr. Walter Millermale27.00001No135081136.7792C89Cherbourg0
239239Clark, Mrs. Walter Miller (Virginia McDowell)female26.00001No135081136.7792C89Cherbourg1
240240Clarke, Mr. Charles Valentinemale29.00001No2003226.0000NaNSouthampton0
241241Clarke, Mrs. Charles V (Ada Maria Winfield)female28.00001No2003226.0000NaNSouthampton1
242242Cleaver, Miss. Alicefemale22.00000No1137811151.5500NaNSouthampton1
243243Clifford, Mr. George QuincymaleNaN0No110465152.0000A14Southampton0
244244Coelho, Mr. Domingos Fernandeomale20.00000NoSOTON/O.Q. 310130737.0500NaNSouthampton0
245245Cohen, Mr. Gurshon 'Gus'male18.00000NoA/5 354038.0500NaNSouthampton1
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247247Coleff, Mr. Pejumale36.00000No34921037.4958NaNSouthampton0
248248Coleff, Mr. Satiomale24.00000No34920937.4958NaNSouthampton0
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253253Collyer, Miss. Marjorie 'Lottie'female8.00000YesC.A. 31921226.2500NaNSouthampton1
254254Collyer, Mr. Harveymale31.00001YesC.A. 31921226.2500NaNSouthampton0
255255Collyer, Mrs. Harvey (Charlotte Annie Tate)female31.00001YesC.A. 31921226.2500NaNSouthampton1
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257257Compton, Mr. Alexander Taylor Jrmale37.00001YesPC 17756183.1583E52Cherbourg0
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259259Conlon, Mr. Thomas Henrymale31.00000No2133237.7333NaNQueenstown0
260260Connaghton, Mr. Michaelmale31.00000No33509737.7500NaNQueenstown0
261261Connolly, Miss. Katefemale22.00000No37037337.7500NaNQueenstown1
262262Connolly, Miss. Katefemale30.00000No33097237.6292NaNQueenstown0
263263Connors, Mr. Patrickmale70.50000No37036937.7500NaNQueenstown0
264264Cook, Mr. Jacobmale43.00000NoA/5 353638.0500NaNSouthampton0
265265Cook, Mrs. (Selena Rogers)female22.00000NoW./C. 14266210.5000F33Southampton1
266266Cor, Mr. Bartolmale35.00000No34923037.8958NaNSouthampton0
267267Cor, Mr. Ivanmale27.00000No34922937.8958NaNSouthampton0
268268Cor, Mr. Liudevitmale19.00000No34923137.8958NaNSouthampton0
269269Corbett, Mrs. Walter H (Irene Colvin)female30.00000No237249213.0000NaNSouthampton0
270270Corey, Mrs. Percy C (Mary Phyllis Elizabeth Mi...femaleNaN0NoF.C.C. 13534221.0000NaNSouthampton0
271271Corn, Mr. Harrymale30.00000NoSOTON/OQ 39209038.0500NaNSouthampton0
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273273Cotterill, Mr. Henry 'Harry'male21.00000No29107211.5000NaNSouthampton0
274274Coutts, Master. Eden Leslie 'Neville'male9.00001YesC.A. 37671315.9000NaNSouthampton1
275275Coutts, Master. William Loch 'William'male3.00001YesC.A. 37671315.9000NaNSouthampton1
276276Coutts, Mrs. William (Winnie 'Minnie' Treanor)female36.00000YesC.A. 37671315.9000NaNSouthampton1
277277Coxon, Mr. Danielmale59.00000No36450037.2500NaNSouthampton0
278278Crafton, Mr. John BertrammaleNaN0No113791126.5500NaNSouthampton0
279279Crease, Mr. Ernest Jamesmale19.00000NoS.P. 346438.1583NaNSouthampton0
280280Cribb, Miss. Laura Alicefemale17.00000Yes371362316.1000NaNSouthampton1
281281Cribb, Mr. John Hatfieldmale44.00000Yes371362316.1000NaNSouthampton0
282282Crosby, Capt. Edward Giffordmale70.00001YesWE/P 5735171.0000B22Southampton0
283283Crosby, Miss. Harriet Rfemale36.00000YesWE/P 5735171.0000B22Southampton1
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285285Culumovic, Mr. Jesomale17.00000No31509038.6625NaNSouthampton0
286286Cumings, Mr. John Bradleymale39.00001NoPC 17599171.2833C85Cherbourg0
287287Cumings, Mrs. John Bradley (Florence Briggs Th...female38.00001NoPC 17599171.2833C85Cherbourg1
288288Cunningham, Mr. Alfred FlemingmaleNaN0No23985320.0000NaNSouthampton0
289289Daher, Mr. Shedidmale22.50000No269837.2250NaNCherbourg0
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297297Danbom, Mr. Ernst Gilbertmale34.00001Yes347080314.4000NaNSouthampton0
298298Danbom, Mrs. Ernst Gilbert (Anna Sigrid Maria ...female28.00001Yes347080314.4000NaNSouthampton0
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302302Dantcheff, Mr. Ristiumale25.00000No34920337.8958NaNSouthampton0
303303Davidson, Mr. Thorntonmale31.00001NoF.C. 12750152.0000B71Southampton0
304304Davidson, Mrs. Thornton (Orian Hays)female27.00001YesF.C. 12750152.0000B71Southampton1
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307307Davies, Mr. Charles Henrymale18.00000NoS.O.C. 14879273.5000NaNSouthampton0
308308Davies, Mr. Evanmale22.00000NoSC/A4 2356838.0500NaNSouthampton0
309309Davies, Mr. John Samuelmale21.00002NoA/4 48871324.1500NaNSouthampton0
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320320Deacon, Mr. Percy Williammale17.00000NoS.O.C. 14879273.5000NaNSouthampton0
321321Dean, Master. Bertram Veremale1.00001YesC.A. 2315320.5750NaNSouthampton1
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326326del Carlo, Mrs. Sebastiano (Argenia Genovesi)female24.00001NoSC/PARIS 2167227.7208NaNCherbourg1
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328328Demetri, Mr. MarinkomaleNaN0No34923837.8958NaNSouthampton0
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480480Guest, Mr. RobertmaleNaN0No37656338.0500NaNSouthampton0
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485485Gustafsson, Mr. Karl Gideonmale19.00000No34706937.7750NaNSouthampton0
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531531Heikkinen, Miss. Lainafemale26.00000NoSTON/O2. 310128237.9250NaNSouthampton1
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537537Herman, Miss. Alicefemale24.00001Yes220845265.0000NaNSouthampton1
538538Herman, Miss. Katefemale24.00001Yes220845265.0000NaNSouthampton1
539539Herman, Mr. Samuelmale49.00001Yes220845265.0000NaNSouthampton0
540540Herman, Mrs. Samuel (Jane Laver)female48.00001Yes220845265.0000NaNSouthampton1
541541Hewlett, Mrs. (Mary D Kingcome)female55.00000No248706216.0000NaNSouthampton1
542542Hickman, Mr. Leonard Markmale24.00002NoS.O.C. 14879273.5000NaNSouthampton0
543543Hickman, Mr. Lewismale32.00002NoS.O.C. 14879273.5000NaNSouthampton0
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13081308Zimmerman, Mr. Leomale29.00000No31508237.8750NaNSouthampton0
\n
" + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic.transform_column(\"parents_children\", lambda row: \"No\" if row.get_value(\"parents_children\") == 0 else \"Yes\")\n" + ], + "metadata": { + "collapsed": false + } } ], "metadata": { diff --git a/docs/tutorials/data_visualization.ipynb b/docs/tutorials/data_visualization.ipynb index 355e411ab..afa25b627 100644 --- a/docs/tutorials/data_visualization.ipynb +++ b/docs/tutorials/data_visualization.ipynb @@ -17,7 +17,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "outputs": [], "source": [ "from safeds.data.tabular.containers import Table\n", @@ -27,8 +27,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "start_time": "2023-03-17T12:33:33.760120Z", - "end_time": "2023-03-17T12:33:33.790272Z" + "start_time": "2023-06-17T18:33:25.276431Z", + "end_time": "2023-06-17T18:33:28.281326Z" } } }, @@ -43,16 +43,26 @@ }, { "cell_type": "code", - "execution_count": null, - "outputs": [], + "execution_count": 2, + "outputs": [ + { + "data": { + "text/plain": " id name sex age \n0 0 Abbing, Mr. Anthony male 42.0 \\\n1 1 Abbott, Master. Eugene Joseph male 13.0 \n2 2 Abbott, Mr. Rossmore Edward male 16.0 \n3 3 Abbott, Mrs. Stanton (Rosa Hunt) female 35.0 \n4 4 Abelseth, Miss. Karen Marie female 16.0 \n5 5 Abelseth, Mr. Olaus Jorgensen male 25.0 \n6 6 Abelson, Mr. Samuel male 30.0 \n7 7 Abelson, Mrs. Samuel (Hannah Wizosky) female 28.0 \n8 8 Abrahamsson, Mr. Abraham August Johannes male 20.0 \n9 9 Abrahim, Mrs. Joseph (Sophie Halaut Easu) female 18.0 \n\n siblings_spouses parents_children ticket travel_class \n0 0 0 C.A. 5547 3 \\\n1 0 2 C.A. 2673 3 \n2 1 1 C.A. 2673 3 \n3 1 1 C.A. 2673 3 \n4 0 0 348125 3 \n5 0 0 348122 3 \n6 1 0 P/PP 3381 2 \n7 1 0 P/PP 3381 2 \n8 0 0 SOTON/O2 3101284 3 \n9 0 0 2657 3 \n\n fare cabin port_embarked survived \n0 7.5500 NaN Southampton 0 \n1 20.2500 NaN Southampton 0 \n2 20.2500 NaN Southampton 0 \n3 20.2500 NaN Southampton 1 \n4 7.6500 NaN Southampton 1 \n5 7.6500 F G63 Southampton 1 \n6 24.0000 NaN Cherbourg 0 \n7 24.0000 NaN Cherbourg 1 \n8 7.9250 NaN Southampton 1 \n9 7.2292 NaN Cherbourg 1 ", + "text/html": "
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idnamesexagesiblings_spousesparents_childrentickettravel_classfarecabinport_embarkedsurvived
00Abbing, Mr. Anthonymale42.000C.A. 554737.5500NaNSouthampton0
11Abbott, Master. Eugene Josephmale13.002C.A. 2673320.2500NaNSouthampton0
22Abbott, Mr. Rossmore Edwardmale16.011C.A. 2673320.2500NaNSouthampton0
33Abbott, Mrs. Stanton (Rosa Hunt)female35.011C.A. 2673320.2500NaNSouthampton1
44Abelseth, Miss. Karen Mariefemale16.00034812537.6500NaNSouthampton1
55Abelseth, Mr. Olaus Jorgensenmale25.00034812237.6500F G63Southampton1
66Abelson, Mr. Samuelmale30.010P/PP 3381224.0000NaNCherbourg0
77Abelson, Mrs. Samuel (Hannah Wizosky)female28.010P/PP 3381224.0000NaNCherbourg1
88Abrahamsson, Mr. Abraham August Johannesmale20.000SOTON/O2 310128437.9250NaNSouthampton1
99Abrahim, Mrs. Joseph (Sophie Halaut Easu)female18.000265737.2292NaNCherbourg1
\n
" + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "titanic.slice_rows(end=10)" ], "metadata": { "collapsed": false, "ExecuteTime": { - "start_time": "2023-03-17T12:33:33.770174Z", - "end_time": "2023-03-17T12:33:33.790272Z" + "start_time": "2023-06-17T18:33:28.285682Z", + "end_time": "2023-06-17T18:33:28.328291Z" } } }, @@ -67,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "outputs": [], "source": [ "titanic_numerical = titanic.remove_columns(\n", @@ -77,8 +87,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "start_time": "2023-03-17T12:33:33.805349Z", - "end_time": "2023-03-17T12:33:33.865018Z" + "start_time": "2023-06-17T18:33:28.312310Z", + "end_time": "2023-06-17T18:33:28.328844Z" } } }, @@ -99,16 +109,26 @@ }, { "cell_type": "code", - "execution_count": null, - "outputs": [], + "execution_count": 4, + "outputs": [ + { + "data": { + "text/plain": "", + "image/png": 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" + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "titanic_numerical.correlation_heatmap()" + "titanic_numerical.plot_correlation_heatmap()" ], "metadata": { "collapsed": false, "ExecuteTime": { - "start_time": "2023-03-17T12:33:33.810380Z", - "end_time": "2023-03-17T12:33:34.035906Z" + "start_time": "2023-06-17T18:33:28.325225Z", + "end_time": "2023-06-17T18:33:28.683441Z" } } }, @@ -140,16 +160,26 @@ }, { "cell_type": "code", - "execution_count": null, - "outputs": [], + "execution_count": 5, + "outputs": [ + { + "data": { + "text/plain": "", + "image/png": 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" + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "titanic_numerical.lineplot(\"survived\", \"fare\")" + "titanic_numerical.plot_lineplot(\"survived\", \"fare\")" ], "metadata": { "collapsed": false, "ExecuteTime": { - "start_time": "2023-03-17T12:33:33.972091Z", - "end_time": "2023-03-17T12:33:34.170190Z" + "start_time": "2023-06-17T18:33:28.702221Z", + "end_time": "2023-06-17T18:33:28.953404Z" } } }, @@ -165,7 +195,12 @@ { "cell_type": "markdown", "source": [ - "## Boxplot" + "## Boxplot\n", + "\n", + "A boxplot is a type of univariate graphic display of the distribution of a numerical dataset. Univariate refers to the analysis of a single variable at a time.\n", + "\n", + "* It displays the minimum, first quartile (25th percentile), median (50th percentile), third quartile (75th percentile), and maximum values.\n", + "* It also shows any potential outliers in the data." ], "metadata": { "collapsed": false @@ -173,23 +208,43 @@ }, { "cell_type": "code", - "execution_count": null, - "outputs": [], + "execution_count": 6, + "outputs": [ + { + "data": { + "text/plain": "", + "image/png": 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" + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "titanic_numerical.get_column(\"age\").boxplot()" + "titanic_numerical.get_column(\"age\").plot_boxplot()" ], "metadata": { "collapsed": false, "ExecuteTime": { - "start_time": "2023-03-14T19:00:57.226271Z", - "end_time": "2023-03-14T19:00:57.302284Z" + "start_time": "2023-06-17T18:33:28.977431Z", + "end_time": "2023-06-17T18:33:29.143982Z" } } }, { "cell_type": "markdown", "source": [ - "## Histogram" + "* The above boxplot depicts the `age` range from 0 to 80 including outliers. It can be deduced that the middle 50% of passengers were between the ages of 20 and 40, because this is where the box lies and is less influenced by outliers.\n", + "* Moreover, the boxplot clearly depicts outliers which are the dots found above the T-shaped whisker. It can further be deduced with the help of the median that half of the passengers were aged below 28 years and the other half were above 28 years." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## Boxplot of all numerical columns" ], "metadata": { "collapsed": false @@ -197,23 +252,53 @@ }, { "cell_type": "code", - "execution_count": null, - "outputs": [], + "execution_count": 7, + "outputs": [ + { + "data": { + "text/plain": "", + "image/png": 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" + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "titanic_numerical.get_column(\"fare\").histogram()" + "titanic_numerical.plot_boxplots()" ], "metadata": { "collapsed": false, "ExecuteTime": { - "start_time": "2023-03-14T19:00:57.312507Z", - "end_time": "2023-03-14T19:00:58.343092Z" + "start_time": "2023-06-17T18:33:29.146080Z", + "end_time": "2023-06-17T18:33:30.540435Z" } } }, { "cell_type": "markdown", "source": [ - "## Scatterplot" + "Let's look at the different boxplots of all numerical columns:\n", + "\n", + "* The second boxplot depicts the number of passengers with `siblings/spouses` aboard the Titanic from 0 to 8. Here, the middle 50% of passengers had either 0 or 1 sibling/spouse and the rest were considered outliers.\n", + "* The `parents/children` boxplot illustrates the count rate of the number of passengers with `parents/children`. More than 80% of the passengers had no parents nor children with them because the upper and lower whiskers including the box are on 0. The rest were considered outliers.\n", + "* The `travel_class` boxplot has its middle 50% of passengers either in the 2nd or 3rd class. The remaining passengers were considered first class.\n", + "* In the `fare` boxplot, the box lies somewhere between 10 and 40. This implies that about half of the passengers paid a passenger fare below 20. The other half paid above 20. It was also dominated by extreme outliers. An extreme outlier is a value 3 times the difference between the 75th percentile and the 25th percentile (3 x IQR).\n", + "* The last boxplot, `survived`, depicts the survival rate of all passengers between 0.0 and 1.0. It is clear that box plot is not a good graphic display for such a variable because it is either a passenger survived (1) or died (0)." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## Histogram\n", + "\n", + "A histogram is a graphic display that divides the x-axis into intervals or bins.\n", + "\n", + "* The height of the bars correspond to the number of values in that bin or interval.\n", + "* Each bar has an area which shows the proportion of values in the range. They can help to predict the probability of getting future measurements." ], "metadata": { "collapsed": false @@ -221,18 +306,130 @@ }, { "cell_type": "code", - "execution_count": null, - "outputs": [], + "execution_count": 8, + "outputs": [ + { + "data": { + "text/plain": "", + "image/png": 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" + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "titanic_numerical.scatterplot(\"age\", \"fare\")\n" + "titanic_numerical.get_column(\"fare\").plot_histogram()" ], "metadata": { "collapsed": false, "ExecuteTime": { - "start_time": "2023-03-14T19:00:58.343092Z", - "end_time": "2023-03-14T19:00:58.457212Z" + "start_time": "2023-06-17T18:33:30.578042Z", + "end_time": "2023-06-17T18:33:30.864985Z" } } + }, + { + "cell_type": "markdown", + "source": [ + "In the `fare` histogram, the tallest bar has the highest number of passengers with a count of 420. While reflecting its base on the x-axis, it can be deduced that about 420 passengers paid a fare of about 10 pounds meanwhile, the last bar to the right indicates that 5 passengers paid a fare of about 510 pounds." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## Histogram of all columns" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 9, + "outputs": [ + { + "data": { + "text/plain": "", + "image/png": 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" + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic_numerical.plot_histograms()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "start_time": "2023-06-17T18:33:30.868832Z", + "end_time": "2023-06-17T18:33:32.576829Z" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Let's look at the different histograms of certain columns:\n", + "* According to the `age` histogram, a majority of the passengers were aged between 20 and 40. If the bars within this range are added up, about 750 passengers fall between this age range. Just a few passengers were above 75 years.\n", + " * It is worth noting, that we can use the `normal distribution` here to approximate the data and future measurements. The normal distribution is a symmetric bell-shaped curve with a continuous range from -infinity to +infinity and has a common value for mean, median, and mode.\n", + "* The `siblings_spouses` histogram illustrates that a majority of the passengers, about 900 had no siblings nor spouses. Meanwhile, for the rest of the passengers, every passenger had at least a sibling or spouse.\n", + "* As per the `parents_children` histogram, most passengers were neither accompanied by a parent nor child.\n", + "* In the histogram for the `travel_class` column, it can be observed that more than 50% of the passengers were categorised as 3rd class passengers. Just about half of the remaining passengers were categorised as 2nd class and the rest were 1st class passengers.\n", + "* The last histogram, `survived` depicts the survival rate of all passengers. About 800 passengers died meanwhile more than 500 passengers survived.\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## Scatterplot\n", + "A scatterplot is a type of graph that displays the relationship between two numerical variables. It provides insights on the distribution pattern, trend, and individual points deviating significantly from the majority of observations called outliers." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 10, + "outputs": [ + { + "data": { + "text/plain": "", + "image/png": 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" + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic_numerical.plot_scatterplot(\"age\", \"fare\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "start_time": "2023-06-17T18:33:32.602078Z", + "end_time": "2023-06-17T18:33:32.786868Z" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "The above scatterplot uses the `age` and `fare` columns from the Titanic dataset to plot each data point that represents a passenger. It can be inferred, that more than 50% of the passengers paid a fare less than 50 pounds. Three passengers of the ages ~35 and 60 paid a fare of about 500 pounds and were considered outliers.\n" + ], + "metadata": { + "collapsed": false + } } ], "metadata": { diff --git a/docs/tutorials/getting_started.ipynb b/docs/tutorials/getting_started.ipynb new file mode 100644 index 000000000..8ffd6b63c --- /dev/null +++ b/docs/tutorials/getting_started.ipynb @@ -0,0 +1,270 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Getting started\n", + "\n", + "This tutorial uses safeds on **titanic passenger data** to predict who will survive and who will not, using sex as a feature for the prediction.\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "1. Load your data into a `Table`, the data is available under `docs/tutorials/data/titanic.csv`:\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 1, + "outputs": [], + "source": [ + "from safeds.data.tabular.containers import Table\n", + "\n", + "titanic = Table.from_csv_file(\"data/titanic.csv\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "start_time": "2023-06-20T13:51:49.792239Z", + "end_time": "2023-06-20T13:51:51.528683Z" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "2. Split the titanic dataset into two tables. A training set, that we will use later to implement a training model to predict the survival of passengers, containing 60% of the data, and a testing set containing the rest of the data.\n", + "Delete the column `survived` from the test set, to be able to predict it later:" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [], + "source": [ + "split_tuple = titanic.split(0.60)\n", + "\n", + "train_table = split_tuple[0]\n", + "test_table = split_tuple[1]\n", + "\n", + "test_table = test_table.remove_columns([\"survived\"])" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "3. Use `OneHotEncoder` to create an encoder, that will be used later to transform the training table.\n", + "* We use `OneHotEncoder` to transform non-numerical categorical values into numerical representations with values of zero or one. In this example we will transform the values of the sex column, hence they will be used in the model for predicting the surviving of passengers.\n", + "* Use the `fit` function of the `OneHotEncoder` to pass the table and the column names, that will be used as features to predict who will survive to the encoder.\n", + "* The names of the column before transformation need to be saved, because `OneHotEncoder` changes the names of the fitted `Column`s:\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [], + "source": [ + "from safeds.data.tabular.transformation import OneHotEncoder\n", + "\n", + "old_column_names = train_table.column_names\n", + "encoder = OneHotEncoder().fit(train_table, [\"sex\"])" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "start_time": "2023-06-20T13:51:51.532902Z", + "end_time": "2023-06-20T13:51:51.540870Z" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "4. Transform the training table using the fitted encoder, and create a set with the new names of the fitted `Column`s:\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [], + "source": [ + "transformed_table = encoder.transform(train_table)\n", + "new_column_names = transformed_table.column_names\n", + "new_columns= set(new_column_names) - set(old_column_names)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "start_time": "2023-06-20T13:51:51.546981Z", + "end_time": "2023-06-20T13:51:51.594283Z" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "5. Tag the `survived` `Column` as the target variable to be predicted. Use the new names of the fitted `Column`s as features, which will be used to make predictions based on the target variable." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [], + "source": [ + "tagged_train_table= transformed_table.tag_columns(\"survived\", feature_names=[\n", + " *new_columns\n", + "])" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "start_time": "2023-06-20T13:51:51.597404Z", + "end_time": "2023-06-20T13:51:51.602283Z" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "6. Use `RandomForest` classifier as a model for the classification. Pass the \"tagged_titanic\" table to the fit function of the model:" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [], + "source": [ + "from safeds.ml.classical.classification import RandomForest\n", + "\n", + "model = RandomForest()\n", + "fitted_model= model.fit(tagged_train_table)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "start_time": "2023-06-20T13:51:51.607645Z", + "end_time": "2023-06-20T13:51:51.861605Z" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "7. Use the fitted random forest model, that we trained on the training dataset to predict the survival rate of passengers in the test dataset.\n", + "Transform the test data with `OneHotEncoder` first, to be able to pass it to the predict function, that uses our fitted random forest model for prediction:" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [ + { + "data": { + "text/plain": " id name sex__male sex__female age \\\n0 785 McGough, Mr. James Robert 1.0 0.0 36.0 \n1 786 McGovern, Miss. Mary 0.0 1.0 NaN \n2 787 McGowan, Miss. Anna 'Annie' 0.0 1.0 15.0 \n3 788 McGowan, Miss. Katherine 0.0 1.0 35.0 \n4 789 McKane, Mr. Peter David 1.0 0.0 46.0 \n.. ... ... ... ... ... \n519 1304 Zabour, Miss. Hileni 0.0 1.0 14.5 \n520 1305 Zabour, Miss. Thamine 0.0 1.0 NaN \n521 1306 Zakarian, Mr. Mapriededer 1.0 0.0 26.5 \n522 1307 Zakarian, Mr. Ortin 1.0 0.0 27.0 \n523 1308 Zimmerman, Mr. Leo 1.0 0.0 29.0 \n\n siblings_spouses parents_children ticket travel_class fare \\\n0 0 0 PC 17473 1 26.2875 \n1 0 0 330931 3 7.8792 \n2 0 0 330923 3 8.0292 \n3 0 0 9232 3 7.7500 \n4 0 0 28403 2 26.0000 \n.. ... ... ... ... ... \n519 1 0 2665 3 14.4542 \n520 1 0 2665 3 14.4542 \n521 0 0 2656 3 7.2250 \n522 0 0 2670 3 7.2250 \n523 0 0 315082 3 7.8750 \n\n cabin port_embarked survived \n0 E25 Southampton 0 \n1 NaN Queenstown 1 \n2 NaN Queenstown 1 \n3 NaN Queenstown 1 \n4 NaN Southampton 0 \n.. ... ... ... \n519 NaN Cherbourg 1 \n520 NaN Cherbourg 1 \n521 NaN Cherbourg 0 \n522 NaN Cherbourg 0 \n523 NaN Southampton 0 \n\n[524 rows x 13 columns]", + "text/html": "
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
idnamesex__malesex__femaleagesiblings_spousesparents_childrentickettravel_classfarecabinport_embarkedsurvived
0785McGough, Mr. James Robert1.00.036.000000PC 17473126.2875E25Southampton0
1786McGovern, Miss. Mary0.01.0NaN0033093137.8792NaNQueenstown1
2787McGowan, Miss. Anna 'Annie'0.01.015.00000033092338.0292NaNQueenstown1
3788McGowan, Miss. Katherine0.01.035.000000923237.7500NaNQueenstown1
4789McKane, Mr. Peter David1.00.046.00000028403226.0000NaNSouthampton0
5790McMahon, Mr. Martin1.00.0NaN0037037237.7500NaNQueenstown0
6791McNamee, Mr. Neal1.00.024.000010376566316.1000NaNSouthampton0
7792McNamee, Mrs. Neal (Eileen O'Leary)0.01.019.000010376566316.1000NaNSouthampton1
8793McNeill, Miss. Bridget0.01.0NaN0037036837.7500NaNQueenstown1
9794Meanwell, Miss. (Marion Ogden)0.01.0NaN00SOTON/O.Q. 39208738.0500NaNSouthampton1
10795Meek, Mrs. Thomas (Annie Louise Rowley)0.01.0NaN0034309538.0500NaNSouthampton1
11796Mellinger, Miss. Madeleine Violet0.01.013.000001250644219.5000NaNSouthampton1
12797Mellinger, Mrs. (Elizabeth Anne Maidment)0.01.041.000001250644219.5000NaNSouthampton1
13798Mellors, Mr. William John1.00.019.000000SW/PP 751210.5000NaNSouthampton0
14799Meo, Mr. Alfonzo1.00.055.500000A.5. 1120638.0500NaNSouthampton0
15800Mernagh, Mr. Robert1.00.0NaN0036870337.7500NaNQueenstown0
16801Meyer, Mr. August1.00.039.000000248723213.0000NaNSouthampton0
17802Meyer, Mr. Edgar Joseph1.00.028.000010PC 17604182.1708NaNCherbourg0
18803Meyer, Mrs. Edgar Joseph (Leila Saks)0.01.0NaN10PC 17604182.1708NaNCherbourg1
19804Midtsjo, Mr. Karl Albert1.00.021.00000034550137.7750NaNSouthampton0
20805Miles, Mr. Frank1.00.0NaN0035930638.0500NaNSouthampton0
21806Millet, Mr. Francis Davis1.00.065.00000013509126.5500E38Southampton0
22807Milling, Mr. Jacob Christian1.00.048.000000234360213.0000NaNSouthampton0
23808Minahan, Dr. William Edward1.00.044.00002019928190.0000C78Queenstown0
24809Minahan, Miss. Daisy E0.01.033.00001019928190.0000C78Queenstown1
25810Minahan, Mrs. William Edward (Lillian E Thorpe)0.01.037.00001019928190.0000C78Queenstown1
26811Mineff, Mr. Ivan1.00.024.00000034923337.8958NaNSouthampton0
27812Minkoff, Mr. Lazar1.00.021.00000034921137.8958NaNSouthampton0
28813Mionoff, Mr. Stoytcho1.00.028.00000034920737.8958NaNSouthampton0
29814Mitchell, Mr. Henry Michael1.00.070.000000C.A. 24580210.5000NaNSouthampton0
30815Mitkoff, Mr. Mito1.00.0NaN0034922137.8958NaNSouthampton0
31816Mock, Mr. Philipp Edmund1.00.030.00001013236157.7500C78Cherbourg0
32817Mockler, Miss. Helen Mary 'Ellie'0.01.0NaN0033098037.8792NaNQueenstown1
33818Moen, Mr. Sigurd Hansen1.00.025.00000034812337.6500F G73Southampton0
34819Molson, Mr. Harry Markland1.00.055.000000113787130.5000C30Southampton0
35820Montvila, Rev. Juozas1.00.027.000000211536213.0000NaNSouthampton0
36821Moor, Master. Meier1.00.06.000001392096312.4750E121Southampton0
37822Moor, Mrs. (Beila)0.01.027.000001392096312.4750E121Southampton1
38823Moore, Mr. Clarence Bloomfield1.00.047.000000113796142.4000NaNSouthampton0
39824Moore, Mr. Leonard Charles1.00.0NaN00A4. 5451038.0500NaNSouthampton0
40825Moran, Miss. Bertha0.01.0NaN10371110324.1500NaNQueenstown1
41826Moran, Mr. Daniel J1.00.0NaN10371110324.1500NaNQueenstown0
42827Moran, Mr. James1.00.0NaN0033087738.4583NaNQueenstown0
43828Moraweck, Dr. Ernest1.00.054.00000029011214.0000NaNSouthampton0
44829Morley, Mr. Henry Samuel ('Mr Henry Marshall')1.00.039.000000250655226.0000NaNSouthampton0
45830Morley, Mr. William1.00.034.00000036450638.0500NaNSouthampton0
46831Morrow, Mr. Thomas Rowan1.00.0NaN0037262237.7500NaNQueenstown0
47832Moss, Mr. Albert Johan1.00.0NaN0031299137.7750NaNSouthampton0
48833Moubarek, Master. Gerios1.00.0NaN112661315.2458NaNCherbourg0
49834Moubarek, Master. Halim Gonios ('William George')1.00.0NaN112661315.2458NaNCherbourg0
50835Moubarek, Mrs. George (Omine 'Amenia' Alexander)0.01.0NaN022661315.2458NaNCherbourg1
51836Moussa, Mrs. (Mantoura Boulos)0.01.0NaN00262637.2292NaNCherbourg1
52837Moutal, Mr. Rahamin Haim1.00.0NaN0037474638.0500NaNSouthampton0
53838Mudd, Mr. Thomas Charles1.00.016.000000S.O./P.P. 3210.5000NaNSouthampton0
54839Mullens, Miss. Katherine 'Katie'0.01.0NaN003585237.7333NaNQueenstown1
55840Mulvihill, Miss. Bertha E0.01.024.00000038265337.7500NaNQueenstown1
56841Murdlin, Mr. Joseph1.00.0NaN00A./5. 323538.0500NaNSouthampton0
57842Murphy, Miss. Katherine 'Kate'0.01.0NaN10367230315.5000NaNQueenstown1
58843Murphy, Miss. Margaret Jane0.01.0NaN10367230315.5000NaNQueenstown1
59844Murphy, Miss. Nora0.01.0NaN0036568315.5000NaNQueenstown1
60845Myhrman, Mr. Pehr Fabian Oliver Malkolm1.00.018.00000034707837.7500NaNSouthampton0
61846Myles, Mr. Thomas Francis1.00.062.00000024027629.6875NaNQueenstown0
62847Naidenoff, Mr. Penko1.00.022.00000034920637.8958NaNSouthampton0
63848Najib, Miss. Adele Kiamie 'Jane'0.01.015.000000266737.2250NaNCherbourg1
64849Nakid, Miss. Maria ('Mary')0.01.01.0000022653315.7417NaNCherbourg1
65850Nakid, Mr. Sahid1.00.020.0000112653315.7417NaNCherbourg0
66851Nakid, Mrs. Said (Waika 'Mary' Mowad)0.01.019.0000112653315.7417NaNCherbourg1
67852Nancarrow, Mr. William Henry1.00.033.000000A./5. 333838.0500NaNSouthampton0
68853Nankoff, Mr. Minko1.00.0NaN0034921837.8958NaNSouthampton0
69854Nasr, Mr. Mustafa1.00.0NaN00265237.2292NaNCherbourg0
70855Nasser, Mr. Nicholas1.00.032.500010237736230.0708NaNCherbourg0
71856Nasser, Mrs. Nicholas (Adele Achem)0.01.014.000010237736230.0708NaNCherbourg1
72857Natsch, Mr. Charles H1.00.037.000001PC 17596129.7000C118Cherbourg0
73858Naughton, Miss. Hannah0.01.0NaN0036523737.7500NaNQueenstown1
74859Navratil, Master. Edmond Roger1.00.02.000011230080226.0000F2Southampton0
75860Navratil, Master. Michel M1.00.03.000011230080226.0000F2Southampton0
76861Navratil, Mr. Michel ('Louis M Hoffman')1.00.036.500002230080226.0000F2Southampton0
77862Nenkoff, Mr. Christo1.00.0NaN0034923437.8958NaNSouthampton0
78863Nesson, Mr. Israel1.00.026.000000244368213.0000F2Southampton0
79864Newell, Miss. Madeleine0.01.031.000010352731113.2750D36Cherbourg1
80865Newell, Miss. Marjorie0.01.023.000010352731113.2750D36Cherbourg1
81866Newell, Mr. Arthur Webster1.00.058.000002352731113.2750D48Cherbourg0
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\n
" + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "encoder = OneHotEncoder().fit(test_table, [\"sex\"])\n", + "transformed_test_table = encoder.transform(test_table)\n", + "\n", + "fitted_model.predict(\n", + " transformed_test_table\n", + ")\n" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "start_time": "2023-06-20T13:51:51.865659Z", + "end_time": "2023-06-20T13:51:52.233431Z" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "7. You can test the accuracy of that model as follows:" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 8, + "outputs": [ + { + "data": { + "text/plain": "0.7745222929936306" + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fitted_model.accuracy(tagged_train_table)\n" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "start_time": "2023-06-20T13:51:52.233053Z", + "end_time": "2023-06-20T13:51:52.254527Z" + } + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/docs/tutorials/machine_learning.ipynb b/docs/tutorials/machine_learning.ipynb index 7a6eaf9a6..a54a91bd0 100644 --- a/docs/tutorials/machine_learning.ipynb +++ b/docs/tutorials/machine_learning.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "outputs": [], "source": [ "from safeds.data.tabular.containers import Table\n", @@ -35,7 +35,11 @@ ")" ], "metadata": { - "collapsed": false + "collapsed": false, + "ExecuteTime": { + "start_time": "2023-06-23T08:54:28.915405Z", + "end_time": "2023-06-23T08:54:28.955647Z" + } } }, { @@ -51,16 +55,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "outputs": [], "source": [ - "from safeds.ml.regression import LinearRegression\n", + "from safeds.ml.classical.regression import LinearRegression\n", "\n", "model = LinearRegression()\n", "fitted_model = model.fit(tagged_table)" ], "metadata": { - "collapsed": false + "collapsed": false, + "ExecuteTime": { + "start_time": "2023-06-23T08:54:28.930451Z", + "end_time": "2023-06-23T08:54:28.956117Z" + } } }, { @@ -76,8 +84,18 @@ }, { "cell_type": "code", - "execution_count": null, - "outputs": [], + "execution_count": 23, + "outputs": [ + { + "data": { + "text/plain": " a b c result\n0 1 2 1 4.0\n1 1 0 4 2.0\n2 0 5 3 6.0\n3 2 2 2 5.0\n4 4 7 1 12.0", + "text/html": "
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447112.0
\n
" + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "test_set = Table({\n", " \"a\": [1, 1, 0, 2, 4],\n", @@ -86,6 +104,303 @@ "\n", "fitted_model.predict(dataset=test_set)\n" ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "start_time": "2023-06-23T08:54:28.938660Z", + "end_time": "2023-06-23T08:54:28.956511Z" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "## Metrics\n", + "\n", + "A machine learning metric, also known as an evaluation metric, is a measure used to assess the performance of a machine learning model on a test set or is used during cross-validation to gain insights about performance and compare different models or parameter settings.\n", + "In `Safe-DS`, the available metrics are: `Accuracy`, `Confusion Matrix`, `F1-Score`, `Precision`, and `Recall`. Before we go through each of these in detail, we need an understanding of the different `components of evaluation metrics`.\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## Components of evaluation metrics\n", + "\n", + "These are distinct elements or parts that contribute to the overall assessment of an evaluation measure.\n", + "* `True positives` TP: the positive tuples that the classifier correctly labeled.\n", + "* `False positives` FP : the negative tuples that were falsely labeled as positive.\n", + "* `True negatives` TN: the negative tuples that the classifier correctly labeled.\n", + "* `False negatives` FN: the positive tuples that were falsely labeled as negative.\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## Confusion Matrix\n", + "A confusion matrix is a table that is used to define the performance of a classification algorithm. It classifies the predictions to be either true positive, true negative, false positive or false negative. It has no Formula.\n", + "* It is useful for evaluating the performance of a classification model, allowing you to assess its accuracy, precision, recall, and other metrics derived from it.\n", + "\n", + "Let's consider a 2x2 confusion matrix made of two classes (0 and 1):" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 24, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion Matrix:\n", + "[[1 1]\n", + " [1 2]]\n" + ] + } + ], + "source": [ + "from sklearn.metrics import confusion_matrix\n", + "\n", + "y_true = [0, 1, 1, 0, 1]\n", + "y_pred = [0, 1, 1, 1, 0]\n", + "\n", + "matrix = confusion_matrix(y_true, y_pred)\n", + "print(\"Confusion Matrix:\")\n", + "print(matrix)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "start_time": "2023-06-23T08:54:28.960698Z", + "end_time": "2023-06-23T08:54:28.972720Z" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "The confusion matrix is divided into four cells, representing different combinations of predicted and true labels:\n", + "* The top-left cell represents the `True Negatives`. The value 1 in this cell indicates that one instance was correctly predicted as the negative class (0).\n", + "* The top-right cell represents the `False Positives`. The value 1 in this cell indicates that one instance was incorrectly predicted as the positive class (1) while the true label is negative (0).\n", + "* The bottom-left cell represents the `False Negatives`. The value 1 in this cell indicates that one instance was incorrectly predicted as the negative class (0) while the true label is positive (1).\n", + "* The bottom-right cell represents the `True Positives`. The value 2 in this cell indicates that two instances were correctly predicted as the positive class (1)." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## Accuracy\n", + "Accuracy, also known as `classification rate`, can be defined as the proportion of correctly classified instances out of the total number of instances. That is, it provides a measure of how well a classification model performs overall.\n", + "* Formula: $ Accuracy = \\frac{TP+TN}{TP+FP+TN+FN} $\n", + "\n", + "Let's consider the same dataset used for deriving the confusion matrix to get the accuracy:" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 25, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 0.6\n" + ] + } + ], + "source": [ + "from sklearn.metrics import accuracy_score\n", + "\n", + "y_true = [0, 1, 1, 0, 1]\n", + "y_pred = [0, 1, 1, 1, 0]\n", + "\n", + "accuracy = accuracy_score(y_true, y_pred)\n", + "print(\"Accuracy:\", accuracy)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "start_time": "2023-06-23T08:54:28.972851Z", + "end_time": "2023-06-23T08:54:29.043605Z" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "The accuracy score 0.6 is calculated as the ratio of the number of correct predictions (3) to the total number of instances (5).\n", + "* `Accuracy` is suitable when the classes are balanced and there is no significant class imbalance. However, accuracy alone may not always provide a complete picture of a model's performance, especially when dealing with imbalanced datasets. In such cases, other metrics like precision and recall may provide a more comprehensive evaluation of the model's performance." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## F1-Score\n", + "The F1-Score is the harmonic mean of precision and recall. That is, it combines precision and recall into a single value.\n", + "* Formula: $ F1-Score = \\frac{2PR}{P+R} $\n", + "\n", + "Let's consider the following dataset to get a better understanding:" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 26, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "F1 Score: 0.6666666666666666\n" + ] + } + ], + "source": [ + "from sklearn.metrics import f1_score\n", + "\n", + "y_true = [0, 1, 1, 0, 1]\n", + "y_pred = [0, 1, 1, 1, 0]\n", + "\n", + "f1 = f1_score(y_true, y_pred)\n", + "print(\"F1 Score:\", f1)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "start_time": "2023-06-23T08:54:28.986093Z", + "end_time": "2023-06-23T08:54:29.044292Z" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Out of the 5 instances in the dataset, 3 have been correctly predicted. However, there is one false positive (4th instance) and one false negative (5th instance). The harmonic mean of precision and recall in this dataset has an f1-score of approximately 0.67.\n", + "* The `F1-score` is suitable when there is an imbalance between the classes, especially when the values of the false positives and false negatives differs.\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## Precision\n", + "The ability of a classification model to identify only the relevant data points. It measures the proportion of correctly predicted positive instances (true positives) out of all instances predicted as positive (both true positives and false positives).\n", + "* Formula: $ P = \\frac{TP}{TP+FP} $\n", + "\n", + "Let's consider the following dataset to get a clearer picture of Precision:" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 27, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Precision: 0.6666666666666666\n" + ] + } + ], + "source": [ + "from sklearn.metrics import precision_score\n", + "\n", + "y_true = [0, 1, 1, 0, 1]\n", + "y_pred = [0, 1, 1, 1, 0]\n", + "\n", + "precision = precision_score(y_true, y_pred)\n", + "print(\"Precision:\", precision)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "start_time": "2023-06-23T08:54:29.000338Z", + "end_time": "2023-06-23T08:54:29.070577Z" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "The classifier correctly predicts 2 true positive and 1 true negative. However, it also has one false positive, predicting a negative instance as positive. Using the above precision formular, we get a precision score of approximately 0.67. A precision score of 1 indicates that all positive predictions made by the model are correct, while a lower score suggests a higher proportion of false positives.\n", + "* Precision is useful when the focus is on minimizing the negative tuples that were falsely labeled as positive." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## Recall\n", + "Also known as `sensitivity` or `true positive rate`, is the ability of a classification model to identify all the relevant data points.\n", + "* Formula: $ R = \\frac{TP}{TP + FN} $\n", + "\n", + "Considering the same dataset used so far, let's calculate the recall score to understand it better:" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 28, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Recall: 0.6666666666666666\n" + ] + } + ], + "source": [ + "from sklearn.metrics import recall_score\n", + "\n", + "y_true = [0, 1, 1, 0, 1]\n", + "y_pred = [0, 1, 1, 1, 0]\n", + "\n", + "recall = recall_score(y_true, y_pred)\n", + "print(\"Recall:\", recall)\n" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "start_time": "2023-06-23T08:54:29.012027Z", + "end_time": "2023-06-23T08:54:29.071537Z" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "The classifier misses one positive instance, resulting in one false negative, where a positive instance is incorrectly classified as negative. Using the above recall formular, we get a recall score of approximately 0.67. A recall score of 1 indicates that all positive instances in the dataset are correctly identified by the model, while a lower score suggests a higher proportion of false negatives.\n", + "* Recall is useful when the focus is on minimizing the positive tuples that were falsely labeled as negative.\n" + ], "metadata": { "collapsed": false } diff --git a/mkdocs.yml b/mkdocs.yml index 11b2f1509..0f4aef178 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -11,13 +11,17 @@ nav: - README.md - Changelog: CHANGELOG.md - Tutorials: + - Getting started: tutorials/getting_started.ipynb - Data Processing: tutorials/data_processing.ipynb - Data Visualization: tutorials/data_visualization.ipynb - Machine Learning: tutorials/machine_learning.ipynb - API Reference: reference/ + - Glossary: glossary.md - Development: - Environment: development/environment.md - - Guidelines: development/guidelines.md + - Project Guidelines: development/project_guidelines.md + - Contributing: https://github.com/Safe-DS/Stdlib/contribute + - Code Review Guide: development/code_review.md # Configuration of MkDocs & Material for MkDocs -------------------------------- @@ -91,6 +95,7 @@ markdown_extensions: - pymdownx.inlinehilite - pymdownx.snippets + # Diagrams - pymdownx.superfences: custom_fences: diff --git a/package-lock.json b/package-lock.json index f7c654948..5b4f4593e 100644 --- a/package-lock.json +++ b/package-lock.json @@ -12,8 +12,8 @@ "@semantic-release/changelog": "^6.0.3", "@semantic-release/exec": "^6.0.3", "@semantic-release/git": "^10.0.1", - "conventional-changelog-conventionalcommits": "^5.0.0", - "semantic-release": "^21.0.0" + "conventional-changelog-conventionalcommits": "^6.1.0", + "semantic-release": "^21.0.6" } }, "node_modules/@babel/code-frame": { @@ -177,21 +177,18 @@ } }, "node_modules/@octokit/auth-token": { - "version": "3.0.3", - "resolved": "https://registry.npmjs.org/@octokit/auth-token/-/auth-token-3.0.3.tgz", - "integrity": "sha512-/aFM2M4HVDBT/jjDBa84sJniv1t9Gm/rLkalaz9htOm+L+8JMj1k9w0CkUdcxNyNxZPlTxKPVko+m1VlM58ZVA==", + "version": "3.0.4", + "resolved": "https://registry.npmjs.org/@octokit/auth-token/-/auth-token-3.0.4.tgz", + "integrity": "sha512-TWFX7cZF2LXoCvdmJWY7XVPi74aSY0+FfBZNSXEXFkMpjcqsQwDSYVv5FhRFaI0V1ECnwbz4j59T/G+rXNWaIQ==", "dev": true, - 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"conventional-changelog-conventionalcommits": "^5.0.0", - "semantic-release": "^21.0.0" + "conventional-changelog-conventionalcommits": "^6.1.0", + "semantic-release": "^21.0.6" } } diff --git a/poetry.lock b/poetry.lock index 788b9252f..1712eeac9 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,10 +1,9 @@ -# This file is automatically @generated by Poetry 1.4.2 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. [[package]] name = "anyio" version = "3.6.2" description = "High level compatibility layer for multiple asynchronous event loop implementations" -category = "dev" optional = false python-versions = ">=3.6.2" files = [ @@ -25,7 +24,6 @@ trio = ["trio (>=0.16,<0.22)"] name = "appnope" version = "0.1.3" description = "Disable App Nap on macOS >= 10.9" -category = "main" optional = false python-versions = "*" files = [ @@ -37,7 +35,6 @@ files = [ name = "argon2-cffi" version = "21.3.0" description = "The secure Argon2 password hashing algorithm." -category = "dev" optional = false python-versions = ">=3.6" files = [ @@ -57,7 +54,6 @@ tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pytest"] name = "argon2-cffi-bindings" version = "21.2.0" description = "Low-level CFFI bindings for Argon2" -category = "dev" optional = false python-versions = ">=3.6" files = [ @@ -95,7 +91,6 @@ tests = ["pytest"] name = "arrow" version = "1.2.3" description = "Better dates & times for Python" -category = "dev" optional = false python-versions = ">=3.6" files = [ @@ -110,7 +105,6 @@ python-dateutil = ">=2.7.0" name = "asttokens" version = "2.2.1" description = "Annotate AST trees with source code positions" -category = "main" optional = false python-versions = "*" files = [ @@ -128,7 +122,6 @@ test = ["astroid", "pytest"] name = "attrs" version = "22.2.0" description = "Classes Without Boilerplate" -category = "dev" optional = false python-versions = ">=3.6" files = [ @@ -147,7 +140,6 @@ tests-no-zope = ["cloudpickle", "cloudpickle", "hypothesis", "hypothesis", "mypy name = "backcall" version = "0.2.0" description = "Specifications for callback functions passed in to an API" -category = "main" optional = false python-versions = "*" files = [ @@ -159,7 +151,6 @@ files = [ name = "beautifulsoup4" version = "4.11.2" description = "Screen-scraping library" -category = "dev" optional = false python-versions = ">=3.6.0" files = [ @@ -178,7 +169,6 @@ lxml = ["lxml"] name = "bleach" version = "6.0.0" description = "An easy safelist-based HTML-sanitizing tool." -category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -197,7 +187,6 @@ css = ["tinycss2 (>=1.1.0,<1.2)"] name = "certifi" version = "2022.12.7" description = "Python package for providing Mozilla's CA Bundle." -category = "dev" optional = false python-versions = ">=3.6" files = [ @@ -209,7 +198,6 @@ files = [ name = "cffi" version = "1.15.1" description = "Foreign Function Interface for Python calling C code." -category = "dev" optional = false python-versions = "*" files = [ @@ -286,7 +274,6 @@ pycparser = "*" name = "charset-normalizer" version = "3.1.0" description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet." -category = "dev" optional = false python-versions = ">=3.7.0" files = [ @@ -371,7 +358,6 @@ files = [ name = "click" version = "8.1.3" description = "Composable command line interface toolkit" -category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -386,7 +372,6 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} name = "colorama" version = "0.4.6" description = "Cross-platform colored terminal text." -category = "main" optional = false python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7" files = [ @@ -398,7 +383,6 @@ files = [ name = "comm" version = "0.1.2" description = "Jupyter Python Comm implementation, for usage in ipykernel, xeus-python etc." -category = "dev" optional = false python-versions = ">=3.6" files = [ @@ -416,7 +400,6 @@ test = ["pytest"] name = "contourpy" version = "1.0.7" description = "Python library for calculating contours of 2D quadrilateral grids" -category = "main" optional = false python-versions = ">=3.8" files = [ @@ -491,7 +474,6 @@ test-no-images = ["pytest"] name = "coverage" version = "7.2.2" description = "Code coverage measurement for Python" -category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -558,7 +540,6 @@ toml = ["tomli"] name = "cycler" version = "0.11.0" description = "Composable style cycles" -category = "main" optional = false python-versions = ">=3.6" files = [ @@ -570,7 +551,6 @@ files = [ name = "debugpy" version = "1.6.6" description = "An implementation of the Debug Adapter Protocol for Python" -category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -597,7 +577,6 @@ files = [ name = "decorator" version = "5.1.1" description = "Decorators for Humans" -category = "main" optional = false python-versions = ">=3.5" files = [ @@ -609,7 +588,6 @@ files = [ name = "defusedxml" version = "0.7.1" description = "XML bomb protection for Python stdlib modules" -category = "dev" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" files = [ @@ -617,11 +595,21 @@ files = [ {file = "defusedxml-0.7.1.tar.gz", hash = "sha256:1bb3032db185915b62d7c6209c5a8792be6a32ab2fedacc84e01b52c51aa3e69"}, ] +[[package]] +name = "et-xmlfile" +version = "1.1.0" +description = "An implementation of lxml.xmlfile for the standard library" +optional = false +python-versions = ">=3.6" +files = [ + {file = "et_xmlfile-1.1.0-py3-none-any.whl", hash = "sha256:a2ba85d1d6a74ef63837eed693bcb89c3f752169b0e3e7ae5b16ca5e1b3deada"}, + {file = "et_xmlfile-1.1.0.tar.gz", hash = "sha256:8eb9e2bc2f8c97e37a2dc85a09ecdcdec9d8a396530a6d5a33b30b9a92da0c5c"}, +] + [[package]] name = "exceptiongroup" version = "1.1.1" description = "Backport of PEP 654 (exception groups)" -category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -636,7 +624,6 @@ test = ["pytest (>=6)"] name = "executing" version = "1.2.0" description = "Get the currently executing AST node of a frame, and other information" -category = "main" optional = false python-versions = "*" files = [ @@ -651,7 +638,6 @@ tests = ["asttokens", "littleutils", "pytest", "rich"] name = "fastjsonschema" version = "2.16.3" description = "Fastest Python implementation of JSON schema" -category = "dev" optional = false python-versions = "*" files = [ @@ -666,7 +652,6 @@ devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benc name = "fonttools" version = "4.39.2" description = "Tools to manipulate font files" -category = "main" optional = false python-versions = ">=3.8" files = [ @@ -692,7 +677,6 @@ woff = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "zopfli (>=0.1.4)"] name = "fqdn" version = "1.5.1" description = "Validates fully-qualified domain names against RFC 1123, so that they are acceptable to modern bowsers" -category = "dev" optional = false python-versions = ">=2.7, !=3.0, !=3.1, !=3.2, !=3.3, !=3.4, <4" files = [ @@ -704,7 +688,6 @@ files = [ name = "ghp-import" version = "2.1.0" description = "Copy your docs directly to the gh-pages branch." -category = "dev" optional = false python-versions = "*" files = [ @@ -722,7 +705,6 @@ dev = ["flake8", "markdown", "twine", "wheel"] name = "griffe" version = "0.25.5" description = "Signatures for entire Python programs. Extract the structure, the frame, the skeleton of your project, to generate API documentation or find breaking changes in your API." -category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -740,7 +722,6 @@ async = ["aiofiles (>=0.7,<1.0)"] name = "idna" version = "3.4" description = "Internationalized Domain Names in Applications (IDNA)" -category = "dev" optional = false python-versions = ">=3.5" files = [ @@ -752,7 +733,6 @@ files = [ name = "iniconfig" version = "2.0.0" description = "brain-dead simple config-ini parsing" -category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -764,7 +744,6 @@ files = [ name = "ipykernel" version = "6.21.3" description = "IPython Kernel for Jupyter" -category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -778,7 +757,7 @@ comm = ">=0.1.1" debugpy = ">=1.6.5" ipython = ">=7.23.1" jupyter-client = ">=6.1.12" -jupyter-core = ">=4.12,<5.0.0 || >=5.1.0" +jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0" matplotlib-inline = ">=0.1" nest-asyncio = "*" packaging = "*" @@ -796,14 +775,13 @@ test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio" [[package]] name = "ipython" -version = "8.12.0" +version = "8.14.0" description = "IPython: Productive Interactive Computing" -category = "main" optional = false -python-versions = ">=3.8" +python-versions = ">=3.9" files = [ - {file = "ipython-8.12.0-py3-none-any.whl", hash = "sha256:1c183bf61b148b00bcebfa5d9b39312733ae97f6dad90d7e9b4d86c8647f498c"}, - {file = "ipython-8.12.0.tar.gz", hash = "sha256:a950236df04ad75b5bc7f816f9af3d74dc118fd42f2ff7e80e8e60ca1f182e2d"}, + {file = "ipython-8.14.0-py3-none-any.whl", hash = "sha256:248aca623f5c99a6635bc3857677b7320b9b8039f99f070ee0d20a5ca5a8e6bf"}, + {file = "ipython-8.14.0.tar.gz", hash = "sha256:1d197b907b6ba441b692c48cf2a3a2de280dc0ac91a3405b39349a50272ca0a1"}, ] [package.dependencies] @@ -837,7 +815,6 @@ test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.21)", "pa name = "ipython-genutils" version = "0.2.0" description = "Vestigial utilities from IPython" -category = "dev" optional = false python-versions = "*" files = [ @@ -849,7 +826,6 @@ files = [ name = "ipywidgets" version = "8.0.4" description = "Jupyter interactive widgets" -category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -871,7 +847,6 @@ test = ["jsonschema", "pytest (>=3.6.0)", "pytest-cov", "pytz"] name = "isoduration" version = "20.11.0" description = "Operations with ISO 8601 durations" -category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -886,7 +861,6 @@ arrow = ">=0.15.0" name = "jedi" version = "0.18.2" description = "An autocompletion tool for Python that can be used for text editors." -category = "main" optional = false python-versions = ">=3.6" files = [ @@ -906,7 +880,6 @@ testing = ["Django (<3.1)", "attrs", "colorama", "docopt", "pytest (<7.0.0)"] name = "jinja2" version = "3.1.2" description = "A very fast and expressive template engine." -category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -924,7 +897,6 @@ i18n = ["Babel (>=2.7)"] name = "joblib" version = "1.2.0" description = "Lightweight pipelining with Python functions" -category = "main" optional = false python-versions = ">=3.7" files = [ @@ -936,7 +908,6 @@ files = [ name = "jsonpointer" version = "2.3" description = "Identify specific nodes in a JSON document (RFC 6901)" -category = "dev" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" files = [ @@ -948,7 +919,6 @@ files = [ name = "jsonschema" version = "4.17.3" description = "An implementation of JSON Schema validation for Python" -category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -976,7 +946,6 @@ format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339- name = "jupyter" version = "1.0.0" description = "Jupyter metapackage. Install all the Jupyter components in one go." -category = "dev" optional = false python-versions = "*" files = [ @@ -997,7 +966,6 @@ qtconsole = "*" name = "jupyter-client" version = "8.0.3" description = "Jupyter protocol implementation and client libraries" -category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -1006,7 +974,7 @@ files = [ ] [package.dependencies] -jupyter-core = ">=4.12,<5.0.0 || >=5.1.0" +jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0" python-dateutil = ">=2.8.2" pyzmq = ">=23.0" tornado = ">=6.2" @@ -1020,7 +988,6 @@ test = ["codecov", "coverage", "ipykernel (>=6.14)", "mypy", "paramiko", "pre-co name = "jupyter-console" version = "6.6.3" description = "Jupyter terminal console" -category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -1032,7 +999,7 @@ files = [ ipykernel = ">=6.14" ipython = "*" jupyter-client = ">=7.0.0" -jupyter-core = ">=4.12,<5.0.0 || >=5.1.0" +jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0" prompt-toolkit = ">=3.0.30" pygments = "*" pyzmq = ">=17" @@ -1045,7 +1012,6 @@ test = ["flaky", "pexpect", "pytest"] name = "jupyter-core" version = "5.3.0" description = "Jupyter core package. A base package on which Jupyter projects rely." -category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -1066,7 +1032,6 @@ test = ["ipykernel", "pre-commit", "pytest", "pytest-cov", "pytest-timeout"] name = "jupyter-events" version = "0.6.3" description = "Jupyter Event System library" -category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -1091,7 +1056,6 @@ test = ["click", "coverage", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>= name = "jupyter-server" version = "2.5.0" description = "The backend—i.e. core services, APIs, and REST endpoints—to Jupyter web applications." -category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -1104,7 +1068,7 @@ anyio = ">=3.1.0" argon2-cffi = "*" jinja2 = "*" jupyter-client = ">=7.4.4" -jupyter-core = ">=4.12,<5.0.0 || >=5.1.0" +jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0" jupyter-events = ">=0.4.0" jupyter-server-terminals = "*" nbconvert = ">=6.4.4" @@ -1127,7 +1091,6 @@ test = ["ipykernel", "pre-commit", "pytest (>=7.0)", "pytest-console-scripts", " name = "jupyter-server-terminals" version = "0.4.4" description = "A Jupyter Server Extension Providing Terminals." -category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -1147,7 +1110,6 @@ test = ["coverage", "jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-cov", name = "jupyterlab-pygments" version = "0.2.2" description = "Pygments theme using JupyterLab CSS variables" -category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -1159,7 +1121,6 @@ files = [ name = "jupyterlab-widgets" version = "3.0.5" description = "Jupyter interactive widgets for JupyterLab" -category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -1171,7 +1132,6 @@ files = [ name = "jupytext" version = "1.14.5" description = "Jupyter notebooks as Markdown documents, Julia, Python or R scripts" -category = "dev" optional = false python-versions = "~=3.6" files = [ @@ -1194,7 +1154,6 @@ toml = ["toml"] name = "kiwisolver" version = "1.4.4" description = "A fast implementation of the Cassowary constraint solver" -category = "main" optional = false python-versions = ">=3.7" files = [ @@ -1272,7 +1231,6 @@ files = [ name = "markdown" version = "3.3.7" description = "Python implementation of Markdown." -category = "dev" optional = false python-versions = ">=3.6" files = [ @@ -1287,7 +1245,6 @@ testing = ["coverage", "pyyaml"] name = "markdown-it-py" version = "2.2.0" description = "Python port of markdown-it. Markdown parsing, done right!" -category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -1312,7 +1269,6 @@ testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"] name = "markupsafe" version = "2.1.2" description = "Safely add untrusted strings to HTML/XML markup." -category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -1372,7 +1328,6 @@ files = [ name = "matplotlib" version = "3.7.1" description = "Python plotting package" -category = "main" optional = false python-versions = ">=3.8" files = [ @@ -1434,7 +1389,6 @@ python-dateutil = ">=2.7" name = "matplotlib-inline" version = "0.1.6" description = "Inline Matplotlib backend for Jupyter" -category = "main" optional = false python-versions = ">=3.5" files = [ @@ -1449,7 +1403,6 @@ traitlets = "*" name = "mdit-py-plugins" version = "0.3.5" description = "Collection of plugins for markdown-it-py" -category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -1469,7 +1422,6 @@ testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"] name = "mdurl" version = "0.1.2" description = "Markdown URL utilities" -category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -1481,7 +1433,6 @@ files = [ name = "mergedeep" version = "1.3.4" description = "A deep merge function for 🐍." -category = "dev" optional = false python-versions = ">=3.6" files = [ @@ -1493,7 +1444,6 @@ files = [ name = "mistune" version = "2.0.5" description = "A sane Markdown parser with useful plugins and renderers" -category = "dev" optional = false python-versions = "*" files = [ @@ -1503,14 +1453,13 @@ files = [ [[package]] name = "mkdocs" -version = "1.4.2" +version = "1.4.3" description = "Project documentation with Markdown." -category = "dev" optional = false python-versions = ">=3.7" files = [ - {file = "mkdocs-1.4.2-py3-none-any.whl", hash = "sha256:c8856a832c1e56702577023cd64cc5f84948280c1c0fcc6af4cd39006ea6aa8c"}, - {file = "mkdocs-1.4.2.tar.gz", hash = "sha256:8947af423a6d0facf41ea1195b8e1e8c85ad94ac95ae307fe11232e0424b11c5"}, + {file = "mkdocs-1.4.3-py3-none-any.whl", hash = "sha256:6ee46d309bda331aac915cd24aab882c179a933bd9e77b80ce7d2eaaa3f689dd"}, + {file = "mkdocs-1.4.3.tar.gz", hash = "sha256:5955093bbd4dd2e9403c5afaf57324ad8b04f16886512a3ee6ef828956481c57"}, ] [package.dependencies] @@ -1533,7 +1482,6 @@ min-versions = ["babel (==2.9.0)", "click (==7.0)", "colorama (==0.4)", "ghp-imp name = "mkdocs-autorefs" version = "0.4.1" description = "Automatically link across pages in MkDocs." -category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -1549,7 +1497,6 @@ mkdocs = ">=1.1" name = "mkdocs-exclude" version = "1.0.2" description = "A mkdocs plugin that lets you exclude files or trees." -category = "dev" optional = false python-versions = "*" files = [ @@ -1561,39 +1508,33 @@ mkdocs = "*" [[package]] name = "mkdocs-gen-files" -version = "0.4.0" +version = "0.5.0" description = "MkDocs plugin to programmatically generate documentation pages during the build" -category = "dev" optional = false -python-versions = ">=3.7,<4.0" +python-versions = ">=3.7" files = [ - 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version = "0.2.6" description = "Measures the displayed width of unicode strings in a terminal" -category = "main" optional = false python-versions = "*" files = [ @@ -3224,7 +3109,6 @@ files = [ name = "webcolors" version = "1.12" description = "A library for working with color names and color values formats defined by HTML and CSS." -category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -3236,7 +3120,6 @@ files = [ name = "webencodings" version = "0.5.1" description = "Character encoding aliases for legacy web content" -category = "dev" optional = false python-versions = "*" files = [ @@ -3248,7 +3131,6 @@ files = [ name = "websocket-client" version = "1.5.1" description = "WebSocket client for Python with low level API options" -category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -3265,7 +3147,6 @@ test = ["websockets"] name = "widgetsnbextension" version = "4.0.5" description = "Jupyter interactive widgets for Jupyter Notebook" -category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -3276,4 +3157,4 @@ files = [ [metadata] lock-version = "2.0" python-versions = "^3.10" -content-hash = "dbd1f2f23c52bb6e883b6aa470410e039ff9bcd7f87fa163a099dfaa27585486" +content-hash = "f3b28a35cdc8774a9e809a4b03c708e703c9d5b6883cd11eba79091f242edcd3" diff --git a/pyproject.toml b/pyproject.toml index 94ff414d0..3c24031e7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,13 +1,13 @@ [tool.poetry] name = "safe-ds" -version = "0.10.0" +version = "0.14.0" description = "A user-friendly library for Data Science in Python." authors = ["Lars Reimann "] license = "MIT" readme = "docs/README.md" repository = "https://github.com/Safe-DS/Stdlib" documentation = "https://safe-ds-stdlib.readthedocs.io" -keywords = ["data science", "machine learning", "usability", "learnability"] +keywords = ["data-science", "machine-learning", "usability", "learnability"] packages = [ { include = "safeds", from = "src"}, ] @@ -20,6 +20,7 @@ pandas = "^2.0.0" pillow = "^9.5.0" scikit-learn = "^1.2.0" seaborn = "^0.12.2" +openpyxl = "^3.1.2" [tool.poetry.group.dev.dependencies] pytest = "^7.2.1" @@ -28,10 +29,10 @@ pytest-cov = "^4.0.0" [tool.poetry.group.docs.dependencies] jupyter = "^1.0.0" mkdocs = "^1.4.2" -mkdocstrings = "^0.20.0" -mkdocstrings-python = "^0.8.3" +mkdocstrings = ">=0.20,<0.23" +mkdocstrings-python = ">=0.8.3,<1.2.0" mkdocs-autorefs = "^0.4.1" -mkdocs-gen-files = "^0.4.0" +mkdocs-gen-files = ">=0.4,<0.6" mkdocs-glightbox = "^0.3.1" mkdocs-literate-nav = "^0.6.0" mkdocs-material = "^9.1.2" @@ -45,6 +46,7 @@ build-backend = "poetry.core.masonry.api" [tool.pytest.ini_options] filterwarnings = [ + "ignore:Deprecated call to `pkg_resources.declare_namespace", "ignore:Jupyter is migrating its paths to use standard platformdirs" ] diff --git a/src/resources/from_csv_file.csv b/src/resources/from_csv_file.csv new file mode 100644 index 000000000..34f6ff8f1 --- /dev/null +++ b/src/resources/from_csv_file.csv @@ -0,0 +1,3 @@ +a,b,c +1,2,1 +0,0,7 diff --git a/src/resources/from_excel_file.xlsx b/src/resources/from_excel_file.xlsx new file mode 100644 index 000000000..f79c95167 Binary files /dev/null and b/src/resources/from_excel_file.xlsx differ diff --git a/src/resources/from_json_file.json b/src/resources/from_json_file.json new file mode 100644 index 000000000..5965ef149 --- /dev/null +++ b/src/resources/from_json_file.json @@ -0,0 +1 @@ +{ "a": { "0": 1, "1": 2, "2": 3 }, "b": { "0": 4, "1": 5, "2": 6 } } diff --git a/src/resources/to_csv_file.csv b/src/resources/to_csv_file.csv new file mode 100644 index 000000000..3390b52f8 --- /dev/null +++ b/src/resources/to_csv_file.csv @@ -0,0 +1,4 @@ +a,b +1,4 +2,5 +3,6 diff --git a/src/resources/to_excel_file.xlsx b/src/resources/to_excel_file.xlsx new file mode 100644 index 000000000..13038e601 Binary files /dev/null and b/src/resources/to_excel_file.xlsx differ diff --git a/src/resources/to_json_file.json b/src/resources/to_json_file.json new file mode 100644 index 000000000..5965ef149 --- /dev/null +++ b/src/resources/to_json_file.json @@ -0,0 +1 @@ +{ "a": { "0": 1, "1": 2, "2": 3 }, "b": { "0": 4, "1": 5, "2": 6 } } diff --git a/src/safeds/data/image/containers/_image.py b/src/safeds/data/image/containers/_image.py index dbe440fe5..7b12dd7c0 100644 --- a/src/safeds/data/image/containers/_image.py +++ b/src/safeds/data/image/containers/_image.py @@ -1,9 +1,13 @@ from __future__ import annotations +import copy import io +import warnings from pathlib import Path -from typing import BinaryIO +from typing import Any, BinaryIO +import PIL +from PIL import ImageEnhance, ImageFilter, ImageOps from PIL.Image import Image as PillowImage from PIL.Image import open as open_image @@ -21,13 +25,13 @@ class Image: """ @staticmethod - def from_jpeg_file(path: str) -> Image: + def from_jpeg_file(path: str | Path) -> Image: """ Create an image from a JPEG file. Parameters ---------- - path : str + path : str | Path The path to the JPEG file. Returns @@ -41,13 +45,13 @@ def from_jpeg_file(path: str) -> Image: ) @staticmethod - def from_png_file(path: str) -> Image: + def from_png_file(path: str | Path) -> Image: """ Create an image from a PNG file. Parameters ---------- - path : str + path : str | Path The path to the PNG file. Returns @@ -82,29 +86,53 @@ def format(self) -> ImageFormat: """ return self._format + @property + def width(self) -> int: + """ + Get the width of the image in pixels. + + Returns + ------- + width : int + The width of the image. + """ + return self._image.width + + @property + def height(self) -> int: + """ + Get the height of the image in pixels. + + Returns + ------- + height : int + The height of the image. + """ + return self._image.height + # ------------------------------------------------------------------------------------------------------------------ # Conversion # ------------------------------------------------------------------------------------------------------------------ - def to_jpeg_file(self, path: str) -> None: + def to_jpeg_file(self, path: str | Path) -> None: """ Save the image as a JPEG file. Parameters ---------- - path : str + path : str | Path The path to the JPEG file. """ Path(path).parent.mkdir(parents=True, exist_ok=True) self._image.save(path, format="jpeg") - def to_png_file(self, path: str) -> None: + def to_png_file(self, path: str | Path) -> None: """ Save the image as a PNG file. Parameters ---------- - path : str + path : str | Path The path to the PNG file. """ Path(path).parent.mkdir(parents=True, exist_ok=True) @@ -114,6 +142,24 @@ def to_png_file(self, path: str) -> None: # IPython integration # ------------------------------------------------------------------------------------------------------------------ + def __eq__(self, other: Any) -> bool: + """ + Compare two images. + + Parameters + ---------- + other: The image to compare to. + + Returns + ------- + equals : bool + Whether the two images contain equal pixel data. + + """ + if not isinstance(other, Image): + return NotImplemented + return self._image.tobytes() == other._image.tobytes() + def _repr_jpeg_(self) -> bytes | None: """ Return a JPEG image as bytes. @@ -151,3 +197,259 @@ def _repr_png_(self) -> bytes | None: self._image.save(buffer, format="png") buffer.seek(0) return buffer.read() + + # ------------------------------------------------------------------------------------------------------------------ + # Transformations + # ------------------------------------------------------------------------------------------------------------------ + + def resize(self, new_width: int, new_height: int) -> Image: + """ + Return a new image that has been resized to a given size. + + Returns + ------- + result : Image + The image with the given width and height + """ + image_copy = copy.deepcopy(self) + image_copy._image = image_copy._image.resize((new_width, new_height)) + return image_copy + + def convert_to_grayscale(self) -> Image: + """ + Convert the image to grayscale. + + Returns + ------- + grayscale_image : Image + The grayscale image. + """ + image_copy = copy.deepcopy(self) + image_copy._image = image_copy._image.convert("L") + return image_copy + + def crop(self, x: int, y: int, width: int, height: int) -> Image: + """ + Return an image that has been cropped to a given bounding rectangle. + + Parameters + ---------- + x: the x coordinate of the top-left corner of the bounding rectangle + y: the y coordinate of the top-left corner of the bounding rectangle + width: the width of the bounding rectangle + height: the height of the bounding rectangle + + Returns + ------- + result : Image + The image with the + """ + image_copy = copy.deepcopy(self) + image_copy._image = image_copy._image.crop((x, y, (x + width), (y + height))) + return image_copy + + def flip_vertically(self) -> Image: + """ + Flip the image vertically (horizontal axis, flips up-down and vice versa). + + Returns + ------- + result : Image + The flipped image. + """ + image_copy = copy.deepcopy(self) + image_copy._image = self._image.transpose(PIL.Image.FLIP_TOP_BOTTOM) + return image_copy + + def flip_horizontally(self) -> Image: + """ + Flip the image horizontally (vertical axis, flips left-right and vice versa). + + Returns + ------- + result : Image + The flipped image. + """ + image_copy = copy.deepcopy(self) + image_copy._image = self._image.transpose(PIL.Image.FLIP_LEFT_RIGHT) + return image_copy + + def adjust_brightness(self, factor: float) -> Image: + """ + Adjust the brightness of an image. + + Parameters + ---------- + factor: float + The brightness factor. + 1.0 will not change the brightness. + Below 1.0 will result in a darker image. + Above 1.0 will resolut in a brighter image. + Has to be bigger than or equal to 0 (black). + + Returns + ------- + result: Image + The Image with adjusted brightness. + """ + if factor < 0: + raise ValueError("Brightness factor has to be 0 or bigger") + elif factor == 1: + warnings.warn( + "Brightness adjustment factor is 1.0, this will not make changes to the image.", + UserWarning, + stacklevel=2, + ) + + image_copy = copy.deepcopy(self) + image_copy._image = ImageEnhance.Brightness(image_copy._image).enhance(factor) + return image_copy + + def adjust_contrast(self, factor: float) -> Image: + """ + Adjust Contrast of image. + + Parameters + ---------- + factor: float + If factor > 1, increase contrast of image. + If factor = 1, no changes will be made. + If factor < 1, make image greyer. + Has to be bigger than or equal to 0 (gray). + + Returns + ------- + New image with adjusted contrast. + """ + if factor < 0: + raise ValueError("Contrast factor has to be 0 or bigger") + elif factor == 1: + warnings.warn( + "Contrast adjustment factor is 1.0, this will not make changes to the image.", + UserWarning, + stacklevel=2, + ) + + image_copy = copy.deepcopy(self) + image_copy._image = ImageEnhance.Contrast(image_copy._image).enhance(factor) + return image_copy + + def adjust_color_balance(self, factor: float) -> Image: + """ + Adjust the image's color balance. + + Parameters + ---------- + factor: float + If factor > 1, increase color balance of image. + If factor = 1, no changes will be made. + If factor < 1, make image greyer. + Has to be bigger than or equal to 0. + + Returns + ------- + image: Image + The new, adjusted image. + """ + if factor < 0: + raise ValueError("Color factor has to be 0 or bigger.") + elif factor == 1: + warnings.warn( + "Color adjustment factor is 1.0, this will not make changes to the image.", + UserWarning, + stacklevel=2, + ) + + image_copy = copy.deepcopy(self) + image_copy._image = ImageEnhance.Color(image_copy._image).enhance(factor) + return image_copy + + def blur(self, radius: int) -> Image: + """ + Return the blurred image. + + Parameters + ---------- + radius : int + Radius is directly proportional to the blur value. The radius is equal to the amount of pixels united in + each direction. A radius of 1 will result in a united box of 9 pixels. + + Returns + ------- + result : Image + The blurred image + """ + image_copy = copy.deepcopy(self) + image_copy._image = image_copy._image.filter(ImageFilter.BoxBlur(radius)) + return image_copy + + def sharpen(self, factor: float) -> Image: + """ + Return the sharpened image. + + Parameters + ---------- + factor: The amount of sharpness to be applied to the image. + Factor 1.0 is considered to be neutral and does not make any changes. + + Returns + ------- + result : Image + The image sharpened by the given factor. + """ + image_copy = copy.deepcopy(self) + image_copy._image = ImageEnhance.Sharpness(image_copy._image).enhance(factor) + return image_copy + + def invert_colors(self) -> Image: + """ + Return the image with inverted colors. + + Returns + ------- + result : Image + The image with inverted colors. + """ + image_copy = copy.deepcopy(self) + image_copy._image = ImageOps.invert(image_copy._image) + return image_copy + + def rotate_right(self) -> Image: + """ + Return the image rotated 90 degrees clockwise. + + Returns + ------- + result : Image + The image rotated 90 degrees clockwise. + """ + image_copy = copy.deepcopy(self) + image_copy._image = image_copy._image.rotate(270, expand=True) + return image_copy + + def rotate_left(self) -> Image: + """ + Return the image rotated 90 degrees counter-clockwise. + + Returns + ------- + result : Image + The image rotated 90 degrees counter-clockwise. + """ + image_copy = copy.deepcopy(self) + image_copy._image = image_copy._image.rotate(90, expand=True) + return image_copy + + def find_edges(self) -> Image: + """ + Return a grayscale image with the highlighted edges. + + Returns + ------- + result : Image + The image with edges found. + """ + image_copy = copy.deepcopy(self) + image_copy = image_copy.convert_to_grayscale() + image_copy._image = image_copy._image.filter(ImageFilter.FIND_EDGES) + return image_copy diff --git a/src/safeds/data/tabular/containers/_column.py b/src/safeds/data/tabular/containers/_column.py index 2a846e03e..c35d08bf0 100644 --- a/src/safeds/data/tabular/containers/_column.py +++ b/src/safeds/data/tabular/containers/_column.py @@ -1,30 +1,34 @@ from __future__ import annotations +import copy import io +from collections.abc import Sequence from numbers import Number -from typing import TYPE_CHECKING, Any +from typing import TYPE_CHECKING, Any, TypeVar, overload import matplotlib.pyplot as plt import numpy as np import pandas as pd import seaborn as sns -from IPython.core.display_functions import DisplayHandle, display from safeds.data.image.containers import Image from safeds.data.image.typing import ImageFormat -from safeds.data.tabular.exceptions import ( +from safeds.data.tabular.typing import ColumnType +from safeds.exceptions import ( ColumnLengthMismatchError, ColumnSizeError, IndexOutOfBoundsError, NonNumericColumnError, ) -from safeds.data.tabular.typing import ColumnType if TYPE_CHECKING: - from collections.abc import Callable, Iterable, Iterator + from collections.abc import Callable, Iterator + +T = TypeVar("T") +R = TypeVar("R") -class Column: +class Column(Sequence[T]): """ A column is a named collection of values. @@ -32,50 +36,235 @@ class Column: ---------- name : str The name of the column. - data : Iterable + data : Sequence[T] The data. - type_ : Optional[ColumnType] - The type of the column. If not specified, the type will be inferred from the data. + + Examples + -------- + >>> from safeds.data.tabular.containers import Column + >>> column = Column("test", [1, 2, 3]) """ + # ------------------------------------------------------------------------------------------------------------------ + # Creation + # ------------------------------------------------------------------------------------------------------------------ + + @staticmethod + def _from_pandas_series(data: pd.Series, type_: ColumnType | None = None) -> Column: + """ + Create a column from a `pandas.Series`. + + Parameters + ---------- + data : pd.Series + The data. + type_ : ColumnType | None + The type. If None, the type is inferred from the data. + + Returns + ------- + column : Column + The created column. + + Examples + -------- + >>> import pandas as pd + >>> from safeds.data.tabular.containers import Column + >>> column = Column._from_pandas_series(pd.Series([1, 2, 3], name="test")) + """ + result = object.__new__(Column) + result._name = data.name + result._data = data + # noinspection PyProtectedMember + result._type = type_ if type_ is not None else ColumnType._from_numpy_data_type(data.dtype) + + return result + # ------------------------------------------------------------------------------------------------------------------ # Dunder methods # ------------------------------------------------------------------------------------------------------------------ - def __init__(self, name: str, data: Iterable, type_: ColumnType | None = None) -> None: + def __init__(self, name: str, data: Sequence[T] | None = None) -> None: + """ + Create a column. + + Parameters + ---------- + name : str + The name of the column. + data : Sequence[T] | None + The data. If None, an empty column is created. + + Examples + -------- + >>> from safeds.data.tabular.containers import Column + >>> column = Column("test", [1, 2, 3]) + """ + if data is None: + data = [] + self._name: str = name - self._data: pd.Series = data if isinstance(data, pd.Series) else pd.Series(data) + self._data: pd.Series = data.rename(name) if isinstance(data, pd.Series) else pd.Series(data, name=name) # noinspection PyProtectedMember - self._type: ColumnType = type_ if type_ is not None else ColumnType._from_numpy_dtype(self._data.dtype) + self._type: ColumnType = ColumnType._from_numpy_data_type(self._data.dtype) + + def __contains__(self, item: Any) -> bool: + return item in self._data def __eq__(self, other: object) -> bool: + """ + Check whether this column is equal to another object. + + Parameters + ---------- + other : object + The other object. + + Returns + ------- + equal : bool + True if the other object is an identical column. False if the other object is a different column. + NotImplemented if the other object is not a column. + + Examples + -------- + >>> from safeds.data.tabular.containers import Column + >>> column1 = Column("test", [1, 2, 3]) + >>> column2 = Column("test", [1, 2, 3]) + >>> column1 == column2 + True + + >>> column3 = Column("test", [3, 4, 5]) + >>> column1 == column3 + False + """ if not isinstance(other, Column): return NotImplemented if self is other: return True return self.name == other.name and self._data.equals(other._data) - def __getitem__(self, index: int) -> Any: - return self.get_value(index) + @overload + def __getitem__(self, index: int) -> T: + ... + + @overload + def __getitem__(self, index: slice) -> Column[T]: + ... + + def __getitem__(self, index: int | slice) -> T | Column[T]: + """ + Return the value of the specified row or rows. + + Parameters + ---------- + index : int | slice + The index of the row, or a slice specifying the start and end index. - def __hash__(self) -> int: - return hash(self._data) + Returns + ------- + value : Any + The single row's value, or rows' values. - def __iter__(self) -> Iterator[Any]: + Raises + ------ + IndexOutOfBoundsError + If the given index or indices do not exist in the column. + + Examples + -------- + >>> from safeds.data.tabular.containers import Column + >>> column = Column("test", [1, 2, 3]) + >>> column[0] + 1 + """ + if isinstance(index, int): + if index < 0 or index >= self._data.size: + raise IndexOutOfBoundsError(index) + return self._data[index] + + if isinstance(index, slice): + if index.start < 0 or index.start > self._data.size: + raise IndexOutOfBoundsError(index) + if index.stop < 0 or index.stop > self._data.size: + raise IndexOutOfBoundsError(index) + data = self._data[index].reset_index(drop=True).rename(self.name) + return Column._from_pandas_series(data, self._type) + + def __iter__(self) -> Iterator[T]: + r""" + Create an iterator for the data of this column. This way e.g. for-each loops can be used on it. + + Returns + ------- + iterator : Iterator[Any] + The iterator. + + Examples + -------- + >>> from safeds.data.tabular.containers import Column + >>> column = Column("test", ["A", "B", "C"]) + >>> string = "" + >>> for val in column: + ... string += val + ", " + >>> string + 'A, B, C, ' + """ return iter(self._data) def __len__(self) -> int: + """ + Return the size of the column. + + Returns + ------- + n_rows : int + The size of the column. + + Examples + -------- + >>> from safeds.data.tabular.containers import Column + >>> column = Column("test", [1, 2, 3]) + >>> len(column) + 3 + """ return len(self._data) def __repr__(self) -> str: - tmp = self._data.to_frame() - tmp.columns = [self.name] - return tmp.__repr__() + """ + Return an unambiguous string representation of this column. + + Returns + ------- + representation : str + The string representation. + + Examples + -------- + >>> from safeds.data.tabular.containers import Column + >>> column = Column("test", [1, 2, 3]) + >>> repr(column) + "Column('test', [1, 2, 3])" + """ + return f"Column({self._name!r}, {list(self._data)!r})" def __str__(self) -> str: - tmp = self._data.to_frame() - tmp.columns = [self.name] - return tmp.__str__() + """ + Return a user-friendly string representation of this column. + + Returns + ------- + representation : str + The string representation. + + Examples + -------- + >>> from safeds.data.tabular.containers import Column + >>> column = Column("test", [1, 2, 3]) + >>> str(column) + "'test': [1, 2, 3]" + """ + return f"{self._name!r}: {list(self._data)!r}" # ------------------------------------------------------------------------------------------------------------------ # Properties @@ -90,9 +279,28 @@ def name(self) -> str: ------- name : str The name of the column. + + Examples + -------- + >>> from safeds.data.tabular.containers import Column + >>> column = Column("test", [1, 2, 3]) + >>> column.name + 'test' """ return self._name + @property + def number_of_rows(self) -> int: + """ + Return the number of elements in the column. + + Returns + ------- + number_of_rows : int + The number of elements. + """ + return len(self._data) + @property def type(self) -> ColumnType: """ @@ -102,6 +310,17 @@ def type(self) -> ColumnType: ------- type : ColumnType The type of the column. + + Examples + -------- + >>> from safeds.data.tabular.containers import Column + >>> column = Column("test", [1, 2, 3]) + >>> column.type + Integer + + >>> column = Column("test", ['a', 'b', 'c']) + >>> column.type + String """ return self._type @@ -109,18 +328,25 @@ def type(self) -> ColumnType: # Getters # ------------------------------------------------------------------------------------------------------------------ - def get_unique_values(self) -> list[Any]: + def get_unique_values(self) -> list[T]: """ Return a list of all unique values in the column. Returns ------- - unique_values : list[any] + unique_values : list[T] List of unique values in the column. + + Examples + -------- + >>> from safeds.data.tabular.containers import Column + >>> column = Column("test", [1, 2, 3, 2, 4, 3]) + >>> column.get_unique_values() + [1, 2, 3, 4] """ return list(self._data.unique()) - def get_value(self, index: int) -> Any: + def get_value(self, index: int) -> T: """ Return column value at specified index, starting at 0. @@ -138,6 +364,13 @@ def get_value(self, index: int) -> Any: ------ IndexOutOfBoundsError If the given index does not exist in the column. + + Examples + -------- + >>> from safeds.data.tabular.containers import Column + >>> column = Column("test", [1, 2, 3]) + >>> column.get_value(1) + 2 """ if index < 0 or index >= self._data.size: raise IndexOutOfBoundsError(index) @@ -148,24 +381,13 @@ def get_value(self, index: int) -> Any: # Information # ------------------------------------------------------------------------------------------------------------------ - def count(self) -> int: - """ - Return the number of elements in the column. - - Returns - ------- - count : int - The number of elements. - """ - return len(self._data) - - def all(self, predicate: Callable[[Any], bool]) -> bool: + def all(self, predicate: Callable[[T], bool]) -> bool: """ Check if all values have a given property. Parameters ---------- - predicate : Callable[[Any], bool]) + predicate : Callable[[T], bool]) Callable that is used to find matches. Returns @@ -173,16 +395,25 @@ def all(self, predicate: Callable[[Any], bool]) -> bool: result : bool True if all match. + Examples + -------- + >>> from safeds.data.tabular.containers import Column + >>> column = Column("test", [1, 2, 3]) + >>> column.all(lambda x: x < 4) + True + + >>> column.all(lambda x: x < 2) + False """ return all(predicate(value) for value in self._data) - def any(self, predicate: Callable[[Any], bool]) -> bool: + def any(self, predicate: Callable[[T], bool]) -> bool: """ Check if any value has a given property. Parameters ---------- - predicate : Callable[[Any], bool]) + predicate : Callable[[T], bool]) Callable that is used to find matches. Returns @@ -190,16 +421,25 @@ def any(self, predicate: Callable[[Any], bool]) -> bool: result : bool True if any match. + Examples + -------- + >>> from safeds.data.tabular.containers import Column + >>> column = Column("test", [1, 2, 3]) + >>> column.any(lambda x: x < 2) + True + + >>> column.any(lambda x: x < 1) + False """ return any(predicate(value) for value in self._data) - def none(self, predicate: Callable[[Any], bool]) -> bool: + def none(self, predicate: Callable[[T], bool]) -> bool: """ Check if no values has a given property. Parameters ---------- - predicate : Callable[[Any], bool]) + predicate : Callable[[T], bool]) Callable that is used to find matches. Returns @@ -207,6 +447,16 @@ def none(self, predicate: Callable[[Any], bool]) -> bool: result : bool True if none match. + Examples + -------- + >>> from safeds.data.tabular.containers import Column + >>> column1 = Column("test", [1, 2, 3]) + >>> column1.none(lambda x: x < 1) + True + + >>> column2 = Column("test", [1, 2, 3]) + >>> column2.none(lambda x: x > 1) + False """ return all(not predicate(value) for value in self._data) @@ -218,6 +468,17 @@ def has_missing_values(self) -> bool: ------- missing_values_exist : bool True if missing values exist. + + Examples + -------- + >>> from safeds.data.tabular.containers import Column + >>> column1 = Column("test", [1, 2, 3, None]) + >>> column1.has_missing_values() + True + + >>> column2 = Column("test", [1, 2, 3]) + >>> column2.has_missing_values() + False """ return self.any(lambda value: value is None or (isinstance(value, Number) and np.isnan(value))) @@ -229,6 +490,8 @@ def rename(self, new_name: str) -> Column: """ Return a new column with a new name. + This column is not modified. + Parameters ---------- new_name : str @@ -238,8 +501,39 @@ def rename(self, new_name: str) -> Column: ------- column : Column A new column with the new name. + + Examples + -------- + >>> from safeds.data.tabular.containers import Column + >>> column = Column("test", [1, 2, 3]) + >>> column.rename("new name") + Column('new name', [1, 2, 3]) + """ + return Column._from_pandas_series(self._data.rename(new_name), self._type) + + def transform(self, transformer: Callable[[T], R]) -> Column[R]: + """ + Apply a transform method to every data point. + + This column is not modified. + + Parameters + ---------- + transformer : Callable[[T], R] + Function that will be applied to all data points. + + Returns + ------- + transformed_column: Column + The transformed column. + + Examples + -------- + >>> from safeds.data.tabular.containers import Column + >>> price = Column("price", [4.99, 5.99, 2.49]) + >>> sale = price.transform(lambda amount: amount * 0.8) """ - return Column(new_name, self._data, self._type) + return Column(self.name, self._data.apply(transformer, convert_dtype=True)) # ------------------------------------------------------------------------------------------------------------------ # Statistics @@ -256,24 +550,39 @@ def correlation_with(self, other_column: Column) -> float: Raises ------ - TypeError + NonNumericColumnError If one of the columns is not numerical. + ColumnLengthMismatchError + If the columns have different lengths. + + Examples + -------- + >>> from safeds.data.tabular.containers import Column + >>> column1 = Column("test", [1, 2, 3]) + >>> column2 = Column("test", [2, 4, 6]) + >>> column1.correlation_with(column2) + 1.0 + + >>> column1 = Column("test", [1, 2, 3]) + >>> column2 = Column("test", [0.5, 4, -6]) + >>> column1.correlation_with(column2) + -0.6404640308067906 """ if not self._type.is_numeric() or not other_column._type.is_numeric(): raise NonNumericColumnError( - f"Columns must be numerical. {self.name} is {self._type}, " - f"{other_column.name} is {other_column._type}.", + f"Columns must be numerical. {self.name} is {self._type}, {other_column.name} is {other_column._type}.", ) if self._data.size != other_column._data.size: raise ColumnLengthMismatchError( - f"{self.name} is of size {self._data.size}, " - f"{other_column.name} is of size {other_column._data.size}.", + f"{self.name} is of size {self._data.size}, {other_column.name} is of size {other_column._data.size}.", ) return self._data.corr(other_column._data) def idness(self) -> float: r""" - Calculate the idness of this column, which we define as. + Calculate the idness of this column. + + We define the idness as follows: $$ \frac{\text{number of different values}}{\text{number of rows}} @@ -288,6 +597,17 @@ def idness(self) -> float: ------ ColumnSizeError If this column is empty. + + Examples + -------- + >>> from safeds.data.tabular.containers import Column + >>> column1 = Column("test", [1, 2, 3]) + >>> column1.idness() + 1.0 + + >>> column2 = Column("test", [1, 2, 3, 2]) + >>> column2.idness() + 0.75 """ if self._data.size == 0: raise ColumnSizeError("> 0", "0") @@ -306,6 +626,13 @@ def maximum(self) -> float: ------ NonNumericColumnError If the data contains non-numerical data. + + Examples + -------- + >>> from safeds.data.tabular.containers import Column + >>> column = Column("test", [1, 2, 3]) + >>> column.maximum() + 3 """ if not self._type.is_numeric(): raise NonNumericColumnError(f"{self.name} is of type {self._type}.") @@ -324,6 +651,13 @@ def mean(self) -> float: ------ NonNumericColumnError If the data contains non-numerical data. + + Examples + -------- + >>> from safeds.data.tabular.containers import Column + >>> column = Column("test", [1, 2, 3]) + >>> column.mean() + 2.0 """ if not self._type.is_numeric(): raise NonNumericColumnError(f"{self.name} is of type {self._type}.") @@ -342,6 +676,18 @@ def median(self) -> float: ------ NonNumericColumnError If the data contains non-numerical data. + + Examples + -------- + >>> from safeds.data.tabular.containers import Column + >>> column = Column("test", [1, 2, 3, 4]) + >>> column.median() + 2.5 + + >>> from safeds.data.tabular.containers import Column + >>> column = Column("test", [1, 2, 3, 4, 5]) + >>> column.median() + 3.0 """ if not self._type.is_numeric(): raise NonNumericColumnError(f"{self.name} is of type {self._type}.") @@ -360,6 +706,13 @@ def minimum(self) -> float: ------ NonNumericColumnError If the data contains non-numerical data. + + Examples + -------- + >>> from safeds.data.tabular.containers import Column + >>> column = Column("test", [1, 2, 3, 4]) + >>> column.minimum() + 1 """ if not self._type.is_numeric(): raise NonNumericColumnError(f"{self.name} is of type {self._type}.") @@ -367,36 +720,67 @@ def minimum(self) -> float: def missing_value_ratio(self) -> float: """ - Return the ratio of null values to the total number of elements in the column. + Return the ratio of missing values to the total number of elements in the column. Returns ------- ratio : float - The ratio of null values to the total number of elements in the column. + The ratio of missing values to the total number of elements in the column. + + Raises + ------ + ColumnSizeError + If the column is empty. + + Examples + -------- + >>> from safeds.data.tabular.containers import Column + >>> column1 = Column("test", [1, 2, 3, 4]) + >>> column1.missing_value_ratio() + 0.0 + + >>> column2 = Column("test", [1, 2, 3, None]) + >>> column2.missing_value_ratio() + 0.25 """ if self._data.size == 0: raise ColumnSizeError("> 0", "0") return self._count_missing_values() / self._data.size - def mode(self) -> Any: + def mode(self) -> list[T]: """ Return the mode of the column. Returns ------- - List : + mode: list[T] Returns a list with the most common values. + + Examples + -------- + >>> from safeds.data.tabular.containers import Column + >>> column1 = Column("test", [1, 2, 3, 3, 4]) + >>> column1.mode() + [3] + + >>> column2 = Column("test", [1, 2, 3, 3, 4, 4]) + >>> column2.mode() + [3, 4] """ return self._data.mode().tolist() def stability(self) -> float: r""" - Calculate the stability of this column, which we define as. + Calculate the stability of this column. + + We define the stability as follows: $$ \frac{\text{number of occurrences of most common non-null value}}{\text{number of non-null values}} $$ + The stability is not definded for a column with only null values. + Returns ------- stability : float @@ -406,9 +790,24 @@ def stability(self) -> float: ------ ColumnSizeError If the column is empty. + + Examples + -------- + >>> from safeds.data.tabular.containers import Column + >>> column1 = Column("test", [1, 1, 2, 3]) + >>> column1.stability() + 0.5 + + >>> column2 = Column("test", [1, 2, 2, 2, 3]) + >>> column2.stability() + 0.6 """ if self._data.size == 0: raise ColumnSizeError("> 0", "0") + + if self.all(lambda x: x is None): + raise ValueError("Stability is not definded for a column with only null values.") + return self._data.value_counts()[self.mode()[0]] / self._data.count() def standard_deviation(self) -> float: @@ -425,6 +824,16 @@ def standard_deviation(self) -> float: NonNumericColumnError If the data contains non-numerical data. + Examples + -------- + >>> from safeds.data.tabular.containers import Column + >>> column1 = Column("test", [1, 2, 3]) + >>> column1.standard_deviation() + 1.0 + + >>> column2 = Column("test", [1, 2, 4, 8, 16]) + >>> column2.standard_deviation() + 6.099180272790763 """ if not self.type.is_numeric(): raise NonNumericColumnError(f"{self.name} is of type {self._type}.") @@ -444,6 +853,12 @@ def sum(self) -> float: NonNumericColumnError If the data contains non-numerical data. + Examples + -------- + >>> from safeds.data.tabular.containers import Column + >>> column = Column("test", [1, 2, 3]) + >>> column.sum() + 6 """ if not self.type.is_numeric(): raise NonNumericColumnError(f"{self.name} is of type {self._type}.") @@ -463,6 +878,12 @@ def variance(self) -> float: NonNumericColumnError If the data contains non-numerical data. + Examples + -------- + >>> from safeds.data.tabular.containers import Column + >>> column = Column("test", [1, 2, 3, 4, 5]) + >>> column.variance() + 2.5 """ if not self.type.is_numeric(): raise NonNumericColumnError(f"{self.name} is of type {self._type}.") @@ -473,7 +894,7 @@ def variance(self) -> float: # Plotting # ------------------------------------------------------------------------------------------------------------------ - def boxplot(self) -> Image: + def plot_boxplot(self) -> Image: """ Plot this column in a boxplot. This function can only plot real numerical data. @@ -484,21 +905,22 @@ def boxplot(self) -> Image: Raises ------ - TypeError - If the column contains non-numerical data or complex data. - """ - for data in self._data: - if not isinstance(data, int) and not isinstance(data, float) and not isinstance(data, complex): - raise NonNumericColumnError(self.name) - if isinstance(data, complex): - raise TypeError( - "The column contains complex data. Boxplots cannot plot the imaginary part of complex " - "data. Please provide a Column with only real numbers", - ) + NonNumericColumnError + If the data contains non-numerical data. + + Examples + -------- + >>> from safeds.data.tabular.containers import Column + >>> column = Column("test", [1, 2, 3]) + >>> boxplot = column.plot_boxplot() + """ + if not self.type.is_numeric(): + raise NonNumericColumnError(f"{self.name} is of type {self._type}.") fig = plt.figure() ax = sns.boxplot(data=self._data) - ax.set(xlabel=self.name) + ax.set(title=self.name) + ax.set_xticks([]) plt.tight_layout() buffer = io.BytesIO() @@ -507,7 +929,7 @@ def boxplot(self) -> Image: buffer.seek(0) return Image(buffer, ImageFormat.PNG) - def histogram(self) -> Image: + def plot_histogram(self) -> Image: """ Plot a column in a histogram. @@ -515,6 +937,12 @@ def histogram(self) -> Image: ------- plot: Image The plot as an image. + + Examples + -------- + >>> from safeds.data.tabular.containers import Column + >>> column = Column("test", [1, 2, 3]) + >>> histogram = column.plot_histogram() """ fig = plt.figure() ax = sns.histplot(data=self._data) @@ -534,23 +962,54 @@ def histogram(self) -> Image: return Image(buffer, ImageFormat.PNG) # ------------------------------------------------------------------------------------------------------------------ - # IPython integration + # Conversion # ------------------------------------------------------------------------------------------------------------------ - def _ipython_display_(self) -> DisplayHandle: + def to_html(self) -> str: + r""" + Return an HTML representation of the column. + + Returns + ------- + output : str + The generated HTML. + + Examples + -------- + >>> from safeds.data.tabular.containers import Column + >>> column = Column("test", [1, 2, 3]) + >>> column.to_html() + '\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
test
01
12
23
' """ - Return a display object for the column to be used in Jupyter Notebooks. + frame = self._data.to_frame() + frame.columns = [self.name] + + return frame.to_html(max_rows=self._data.size, max_cols=1) + + # ------------------------------------------------------------------------------------------------------------------ + # IPython integration + # ------------------------------------------------------------------------------------------------------------------ + + def _repr_html_(self) -> str: + r""" + Return an HTML representation of the column. Returns ------- - output : DisplayHandle - Output object. + output : str + The generated HTML. + + Examples + -------- + >>> from safeds.data.tabular.containers import Column + >>> column = Column("col_1", ['a', 'b', 'c']) + >>> column._repr_html_() + '
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
col_1
0a
1b
2c
\n
' """ - tmp = self._data.to_frame() - tmp.columns = [self.name] + frame = self._data.to_frame() + frame.columns = [self.name] - with pd.option_context("display.max_rows", tmp.shape[0], "display.max_columns", tmp.shape[1]): - return display(tmp) + return frame.to_html(max_rows=self._data.size, max_cols=1, notebook=True) # ------------------------------------------------------------------------------------------------------------------ # Other @@ -564,5 +1023,28 @@ def _count_missing_values(self) -> int: ------- count : int The number of null values. + + Examples + -------- + >>> from safeds.data.tabular.containers import Column + >>> column = Column("col_1", [None, 'a', None]) + >>> column._count_missing_values() + 2 """ return self._data.isna().sum() + + # ------------------------------------------------------------------------------------------------------------------ + # Helpers + # ------------------------------------------------------------------------------------------------------------------ + + def _copy(self) -> Column: + """ + Return a copy of this column. + + Returns + ------- + column : Column + The copy of this column. + + """ + return copy.deepcopy(self) diff --git a/src/safeds/data/tabular/containers/_row.py b/src/safeds/data/tabular/containers/_row.py index 61b84331e..141f0503c 100644 --- a/src/safeds/data/tabular/containers/_row.py +++ b/src/safeds/data/tabular/containers/_row.py @@ -1,46 +1,183 @@ -from collections.abc import Iterable, Iterator -from hashlib import md5 -from typing import Any +from __future__ import annotations + +import copy +from collections.abc import Mapping +from typing import TYPE_CHECKING, Any import pandas as pd -from IPython.core.display_functions import DisplayHandle, display -from pandas.core.util.hashing import hash_pandas_object -from safeds.data.tabular.exceptions import UnknownColumnNameError from safeds.data.tabular.typing import ColumnType, Schema +from safeds.exceptions import UnknownColumnNameError + +if TYPE_CHECKING: + from collections.abc import Iterator -class Row: +class Row(Mapping[str, Any]): """ - A row is a collection of values, where each value is associated with a column name. + A row is a collection of named values. Parameters ---------- - data : Iterable - The data. - schema : Schema - The schema of the row. + data : Mapping[str, Any] | None + The data. If None, an empty row is created. + + Examples + -------- + >>> from safeds.data.tabular.containers import Row + >>> row = Row({"a": 1, "b": 2}) """ # ------------------------------------------------------------------------------------------------------------------ - # Dunder methods + # Creation # ------------------------------------------------------------------------------------------------------------------ - def __init__(self, data: Iterable, schema: Schema | None = None): - self._data: pd.Series = data if isinstance(data, pd.Series) else pd.Series(data) - self._data = self._data.reset_index(drop=True) + @staticmethod + def from_dict(data: dict[str, Any]) -> Row: + """ + Create a row from a dictionary that maps column names to column values. + + Parameters + ---------- + data : dict[str, Any] + The data. - self._schema: Schema - if schema is not None: - self._schema = schema - else: - column_names = [f"column_{i}" for i in range(len(self._data))] - dataframe = self._data.to_frame().T - dataframe.columns = column_names + Returns + ------- + row : Row + The created row. + + Examples + -------- + >>> from safeds.data.tabular.containers import Row + >>> row = Row.from_dict({"a": 1, "b": 2}) + """ + return Row(data) + + @staticmethod + def _from_pandas_dataframe(data: pd.DataFrame, schema: Schema | None = None) -> Row: + """ + Create a row from a `pandas.DataFrame`. + + Parameters + ---------- + data : pd.DataFrame + The data. + schema : Schema | None + The schema. If None, the schema is inferred from the data. + + Returns + ------- + row : Row + The created row. + + Raises + ------ + ValueError + If the dataframe does not contain exactly one row. + + Examples + -------- + >>> import pandas as pd + >>> from safeds.data.tabular.containers import Row + >>> row = Row._from_pandas_dataframe(pd.DataFrame({"a": [1], "b": [2]})) + """ + if data.shape[0] != 1: + raise ValueError("The dataframe has to contain exactly one row.") + + data = data.reset_index(drop=True) + + result = object.__new__(Row) + result._data = data + + if schema is None: # noinspection PyProtectedMember - self._schema = Schema._from_dataframe(dataframe) + result._schema = Schema._from_pandas_dataframe(data) + else: + result._schema = schema + + return result + + # ------------------------------------------------------------------------------------------------------------------ + # Dunder methods + # ------------------------------------------------------------------------------------------------------------------ + + def __init__(self, data: Mapping[str, Any] | None = None) -> None: + """ + Create a row from a mapping of column names to column values. + + Parameters + ---------- + data : Mapping[str, Any] | None + The data. If None, an empty row is created. + + Examples + -------- + >>> from safeds.data.tabular.containers import Row + >>> row = Row({"a": 1, "b": 2}) + """ + if data is None: + data = {} + + data = {key: [value] for key, value in data.items()} + + self._data: pd.DataFrame = pd.DataFrame(data) + # noinspection PyProtectedMember + self._schema: Schema = Schema._from_pandas_dataframe(self._data) + + def __contains__(self, obj: Any) -> bool: + """ + Check whether the row contains an object as key. + + Parameters + ---------- + obj : Any + The object. + + Returns + ------- + has_column : bool + True, if the row contains the object as key, False otherwise. + + Examples + -------- + >>> from safeds.data.tabular.containers import Row + >>> row = Row({"a": 1, "b": 2}) + >>> "a" in row + True + + >>> "c" in row + False + """ + return isinstance(obj, str) and self.has_column(obj) def __eq__(self, other: Any) -> bool: + """ + Check whether this row is equal to another object. + + Parameters + ---------- + other : Any + The other object. + + Returns + ------- + equal : bool + True if the other object is an identical row. False if the other object is a different row. NotImplemented + if the other object is not a row. + + Examples + -------- + >>> from safeds.data.tabular.containers import Row + >>> row1 = Row({"a": 1, "b": 2}) + >>> row2 = Row({"a": 1, "b": 2}) + >>> row1 == row2 + True + + >>> row3 = Row({"a": 1, "b": 3}) + >>> row1 == row3 + False + """ if not isinstance(other, Row): return NotImplemented if self is other: @@ -48,34 +185,155 @@ def __eq__(self, other: Any) -> bool: return self._schema == other._schema and self._data.equals(other._data) def __getitem__(self, column_name: str) -> Any: - return self.get_value(column_name) + """ + Return the value of a specified column. - def __hash__(self) -> int: - data_hash_string = md5(hash_pandas_object(self._data, index=True).values).hexdigest() - column_names_frozenset = frozenset(self.get_column_names()) + Parameters + ---------- + column_name : str + The column name. - return hash((data_hash_string, column_names_frozenset)) + Returns + ------- + value : Any + The column value. + + Raises + ------ + UnknownColumnNameError + If the row does not contain the specified column. + + Examples + -------- + >>> from safeds.data.tabular.containers import Row + >>> row = Row({"a": 1, "b": 2}) + >>> row["a"] + 1 + """ + return self.get_value(column_name) def __iter__(self) -> Iterator[Any]: - return iter(self.get_column_names()) + """ + Create an iterator for the column names of this row. + + Returns + ------- + iterator : Iterator[Any] + The iterator. + + Examples + -------- + >>> from safeds.data.tabular.containers import Row + >>> row = Row({"a": 1, "b": 2}) + >>> list(row) + ['a', 'b'] + """ + return iter(self.column_names) def __len__(self) -> int: - return len(self._data) + """ + Return the number of columns in this row. + + Returns + ------- + number_of_columns : int + The number of columns. + + Examples + -------- + >>> from safeds.data.tabular.containers import Row + >>> row = Row({"a": 1, "b": 2}) + >>> len(row) + 2 + """ + return self._data.shape[1] def __repr__(self) -> str: - tmp = self._data.to_frame().T - tmp.columns = self.get_column_names() - return tmp.__repr__() + """ + Return an unambiguous string representation of this row. + + Returns + ------- + representation : str + The string representation. + + Examples + -------- + >>> from safeds.data.tabular.containers import Row + >>> row = Row({"a": 1}) + >>> repr(row) + "Row({'a': 1})" + """ + return f"Row({self!s})" def __str__(self) -> str: - tmp = self._data.to_frame().T - tmp.columns = self.get_column_names() - return tmp.__str__() + """ + Return a user-friendly string representation of this row. + + Returns + ------- + representation : str + The string representation. + + Examples + -------- + >>> from safeds.data.tabular.containers import Row + >>> row = Row({"a": 1}) + >>> str(row) + "{'a': 1}" + """ + match len(self): + case 0: + return "{}" + case 1: + return str(self.to_dict()) + case _: + lines = (f" {name!r}: {value!r}" for name, value in self.to_dict().items()) + joined = ",\n".join(lines) + return f"{{\n{joined}\n}}" # ------------------------------------------------------------------------------------------------------------------ # Properties # ------------------------------------------------------------------------------------------------------------------ + @property + def column_names(self) -> list[str]: + """ + Return a list of all column names in the row. + + Returns + ------- + column_names : list[str] + The column names. + + Examples + -------- + >>> from safeds.data.tabular.containers import Row + >>> row = Row({"a": 1, "b": 2}) + >>> row.column_names + ['a', 'b'] + """ + return self._schema.column_names + + @property + def number_of_column(self) -> int: + """ + Return the number of columns in this row. + + Returns + ------- + number_of_column : int + The number of columns. + + Examples + -------- + >>> from safeds.data.tabular.containers import Row + >>> row = Row({"a": 1, "b": 2}) + >>> row.number_of_column + 2 + """ + return self._data.shape[1] + @property def schema(self) -> Schema: """ @@ -85,6 +343,12 @@ def schema(self) -> Schema: ------- schema : Schema The schema. + + Examples + -------- + >>> from safeds.data.tabular.containers import Row + >>> row = Row({"a": 1, "b": 2}) + >>> schema = row.schema """ return self._schema @@ -103,55 +367,60 @@ def get_value(self, column_name: str) -> Any: Returns ------- - value : - The value of the column. + value : Any + The column value. + + Raises + ------ + UnknownColumnNameError + If the row does not contain the specified column. + + Examples + -------- + >>> from safeds.data.tabular.containers import Row + >>> row = Row({"a": 1, "b": 2}) + >>> row.get_value("a") + 1 """ - if not self._schema.has_column(column_name): + if not self.has_column(column_name): raise UnknownColumnNameError([column_name]) - # noinspection PyProtectedMember - return self._data[self._schema._get_column_index_by_name(column_name)] + + return self._data.loc[0, column_name] def has_column(self, column_name: str) -> bool: """ - Return whether the row contains a given column. - - Alias for self.schema.hasColumn(column_name: str) -> bool. + Check whether the row contains a given column. Parameters ---------- column_name : str - The name of the column. + The column name. Returns ------- - contains : bool - True, if row contains the column. + has_column : bool + True, if the row contains the column, False otherwise. + + Examples + -------- + >>> from safeds.data.tabular.containers import Row + >>> row = Row({"a": 1, "b": 2}) + >>> row.has_column("a") + True + + >>> row.has_column("c") + False """ return self._schema.has_column(column_name) - def get_column_names(self) -> list[str]: - """ - Return a list of all column names saved in this schema. - - Alias for self.schema.get_column_names() -> list[str]. - - Returns - ------- - column_names : list[str] - The column names. - """ - return self._schema.get_column_names() - - def get_type_of_column(self, column_name: str) -> ColumnType: + def get_column_type(self, column_name: str) -> ColumnType: """ - Return the type of a specified column. - - Alias for self.schema.get_type_of_column(column_name: str) -> ColumnType. + Return the type of the specified column. Parameters ---------- column_name : str - The name of the column. + The column name. Returns ------- @@ -160,41 +429,83 @@ def get_type_of_column(self, column_name: str) -> ColumnType: Raises ------ - ColumnNameError - If the specified target column name does not exist. + UnknownColumnNameError + If the row does not contain the specified column. + + Examples + -------- + >>> from safeds.data.tabular.containers import Row + >>> row = Row({"a": 1, "b": 2}) + >>> row.get_column_type("a") + Integer """ - return self._schema.get_type_of_column(column_name) + return self._schema.get_column_type(column_name) # ------------------------------------------------------------------------------------------------------------------ - # Information + # Conversion # ------------------------------------------------------------------------------------------------------------------ - def count(self) -> int: + def to_dict(self) -> dict[str, Any]: """ - Return the number of columns in this row. + Return a dictionary that maps column names to column values. Returns ------- - count : int - The number of columns. + data : dict[str, Any] + Dictionary representation of the row. + + Examples + -------- + >>> from safeds.data.tabular.containers import Row + >>> row = Row({"a": 1, "b": 2}) + >>> row.to_dict() + {'a': 1, 'b': 2} """ - return len(self._data) + return {column_name: self.get_value(column_name) for column_name in self.column_names} + + def to_html(self) -> str: + """ + Return an HTML representation of the row. + + Returns + ------- + output : str + The generated HTML. + + Examples + -------- + >>> from safeds.data.tabular.containers import Row + >>> row = Row({"a": 1, "b": 2}) + >>> html = row.to_html() + """ + return self._data.to_html(max_rows=1, max_cols=self._data.shape[1]) # ------------------------------------------------------------------------------------------------------------------ # IPython integration # ------------------------------------------------------------------------------------------------------------------ - def _ipython_display_(self) -> DisplayHandle: + def _repr_html_(self) -> str: """ - Return a display object for the column to be used in Jupyter Notebooks. + Return an HTML representation of the row. Returns ------- - output : DisplayHandle - Output object. + output : str + The generated HTML. """ - tmp = self._data.to_frame().T - tmp.columns = self.get_column_names() + return self._data.to_html(max_rows=1, max_cols=self._data.shape[1], notebook=True) - with pd.option_context("display.max_rows", tmp.shape[0], "display.max_columns", tmp.shape[1]): - return display(tmp) + # ------------------------------------------------------------------------------------------------------------------ + # Helpers + # ------------------------------------------------------------------------------------------------------------------ + + def _copy(self) -> Row: + """ + Return a copy of this row. + + Returns + ------- + copy : Row + The copy of this row. + """ + return copy.deepcopy(self) diff --git a/src/safeds/data/tabular/containers/_table.py b/src/safeds/data/tabular/containers/_table.py index 2b45928c3..176585411 100644 --- a/src/safeds/data/tabular/containers/_table.py +++ b/src/safeds/data/tabular/containers/_table.py @@ -1,37 +1,41 @@ from __future__ import annotations +import copy import functools import io +import warnings from pathlib import Path -from typing import TYPE_CHECKING, Any +from typing import TYPE_CHECKING, Any, TypeVar import matplotlib.pyplot as plt import numpy as np +import openpyxl import pandas as pd import seaborn as sns -from IPython.core.display_functions import DisplayHandle, display -from pandas import DataFrame, Series +from pandas import DataFrame from scipy import stats from safeds.data.image.containers import Image from safeds.data.image.typing import ImageFormat -from safeds.data.tabular.exceptions import ( +from safeds.data.tabular.typing import ColumnType, Schema +from safeds.exceptions import ( ColumnLengthMismatchError, ColumnSizeError, DuplicateColumnNameError, IndexOutOfBoundsError, - MissingDataError, NonNumericColumnError, SchemaMismatchError, UnknownColumnNameError, + WrongFileExtensionError, ) -from safeds.data.tabular.typing import ColumnType, Schema from ._column import Column from ._row import Row if TYPE_CHECKING: - from collections.abc import Callable, Iterable + from collections.abc import Callable, Mapping, Sequence + + from safeds.data.tabular.transformation import InvertibleTableTransformer, TableTransformer from ._tagged_table import TaggedTable @@ -41,17 +45,32 @@ class Table: """ A table is a two-dimensional collection of data. It can either be seen as a list of rows or as a list of columns. + To create a `Table` call the constructor or use one of the following static methods: + + | Method | Description | + | ---------------------------------------------------------------------------- | -------------------------------------- | + | [from_csv_file][safeds.data.tabular.containers._table.Table.from_csv_file] | Create a table from a CSV file. | + | [from_json_file][safeds.data.tabular.containers._table.Table.from_json_file] | Create a table from a JSON file. | + | [from_dict][safeds.data.tabular.containers._table.Table.from_dict] | Create a table from a dictionary. | + | [from_columns][safeds.data.tabular.containers._table.Table.from_columns] | Create a table from a list of columns. | + | [from_rows][safeds.data.tabular.containers._table.Table.from_rows] | Create a table from a list of rows. | + + Note: When removing the last column of the table, the `number_of_columns` property will be set to 0. + Parameters ---------- - data : typing.Iterable - The data. - schema : Optional[Schema] - The schema of the table. If not specified, the schema will be inferred from the data. + data : Mapping[str, Sequence[Any]] | None + The data. If None, an empty table is created. Raises ------ - MissingSchemaError - If the table is empty and no schema is specified. + ColumnLengthMismatchError + If columns have different lengths. + + Examples + -------- + >>> from safeds.data.tabular.containers import Table + >>> table = Table({"a": [1, 2, 3], "b": [4, 5, 6]}) """ # ------------------------------------------------------------------------------------------------------------------ @@ -59,13 +78,13 @@ class Table: # ------------------------------------------------------------------------------------------------------------------ @staticmethod - def from_csv_file(path: str) -> Table: + def from_csv_file(path: str | Path) -> Table: """ Read data from a CSV file into a table. Parameters ---------- - path : str + path : str | Path The path to the CSV file. Returns @@ -77,22 +96,81 @@ def from_csv_file(path: str) -> Table: ------ FileNotFoundError If the specified file does not exist. - ValueError - If the file could not be read. + WrongFileExtensionError + If the file is not a csv file. + + Examples + -------- + >>> from safeds.data.tabular.containers import Table + >>> Table.from_csv_file('./src/resources/from_csv_file.csv') + a b c + 0 1 2 1 + 1 0 0 7 + """ + path = Path(path) + if path.suffix != ".csv": + raise WrongFileExtensionError(path, ".csv") + if path.exists(): + with path.open() as f: + if f.read().replace("\n", "") == "": + return Table() + + return Table._from_pandas_dataframe(pd.read_csv(path)) + else: + raise FileNotFoundError(f'File "{path}" does not exist') + + @staticmethod + def from_excel_file(path: str | Path) -> Table: """ + Read data from an Excel file into a table. + + Valid file extensions are `.xls`, '.xlsx', `.xlsm`, `.xlsb`, `.odf`, `.ods` and `.odt`. + + Parameters + ---------- + path : str | Path + The path to the Excel file. + + Returns + ------- + table : Table + The table created from the Excel file. + + Raises + ------ + FileNotFoundError + If the specified file does not exist. + WrongFileExtensionError + If the file is not an Excel file. + + Examples + -------- + >>> from safeds.data.tabular.containers import Table + >>> Table.from_excel_file('./src/resources/from_excel_file.xlsx') + a b + 0 1 4 + 1 2 5 + 2 3 6 + """ + path = Path(path) + excel_extensions = [".xls", ".xlsx", ".xlsm", ".xlsb", ".odf", ".ods", ".odt"] + if path.suffix not in excel_extensions: + raise WrongFileExtensionError(path, excel_extensions) try: - return Table(pd.read_csv(path)) + return Table._from_pandas_dataframe( + pd.read_excel(path, engine="openpyxl", usecols=lambda colname: "Unnamed" not in colname), + ) except FileNotFoundError as exception: raise FileNotFoundError(f'File "{path}" does not exist') from exception @staticmethod - def from_json_file(path: str) -> Table: + def from_json_file(path: str | Path) -> Table: """ Read data from a JSON file into a table. Parameters ---------- - path : str + path : str | Path The path to the JSON file. Returns @@ -104,13 +182,29 @@ def from_json_file(path: str) -> Table: ------ FileNotFoundError If the specified file does not exist. - ValueError - If the file could not be read. - """ - try: - return Table(pd.read_json(path)) - except FileNotFoundError as exception: - raise FileNotFoundError(f'File "{path}" does not exist') from exception + WrongFileExtensionError + If the file is not a JSON file. + + Examples + -------- + >>> from safeds.data.tabular.containers import Table + >>> Table.from_json_file('./src/resources/from_json_file.json') + a b + 0 1 4 + 1 2 5 + 2 3 6 + """ + path = Path(path) + if path.suffix != ".json": + raise WrongFileExtensionError(path, ".json") + if path.exists(): + with path.open() as f: + if f.read().replace("\n", "") in ("", "{}"): + return Table() + + return Table._from_pandas_dataframe(pd.read_json(path)) + else: + raise FileNotFoundError(f'File "{path}" does not exist') @staticmethod def from_dict(data: dict[str, list[Any]]) -> Table: @@ -131,22 +225,17 @@ def from_dict(data: dict[str, list[Any]]) -> Table: ------ ColumnLengthMismatchError If columns have different lengths. - """ - # Validation - expected_length: int | None = None - for column_values in data.values(): - if expected_length is None: - expected_length = len(column_values) - elif len(column_values) != expected_length: - raise ColumnLengthMismatchError( - "\n".join(f"{column_name}: {len(column_values)}" for column_name, column_values in data.items()), - ) - # Implementation - dataframe: DataFrame = pd.DataFrame() - for column_name, column_values in data.items(): - dataframe[column_name] = column_values - return Table(dataframe) + Examples + -------- + >>> from safeds.data.tabular.containers import Table + >>> d = {'a': [1, 2], 'b': [3, 4]} + >>> Table.from_dict(d) + a b + 0 1 3 + 1 2 4 + """ + return Table(data) @staticmethod def from_columns(columns: list[Column]) -> Table: @@ -167,17 +256,34 @@ def from_columns(columns: list[Column]) -> Table: ------ ColumnLengthMismatchError If any of the column sizes does not match with the others. + DuplicateColumnNameError + If multiple columns have the same name. + + Examples + -------- + >>> from safeds.data.tabular.containers import Column, Table + >>> col1 = Column("a", [1, 2, 3]) + >>> col2 = Column("b", [4, 5, 6]) + >>> Table.from_columns([col1, col2]) + a b + 0 1 4 + 1 2 5 + 2 3 6 """ dataframe: DataFrame = pd.DataFrame() + column_names = [] for column in columns: if column._data.size != columns[0]._data.size: raise ColumnLengthMismatchError( "\n".join(f"{column.name}: {column._data.size}" for column in columns), ) + if column.name in column_names: + raise DuplicateColumnNameError(column.name) + column_names.append(column.name) dataframe[column.name] = column._data - return Table(dataframe) + return Table._from_pandas_dataframe(dataframe) @staticmethod def from_rows(rows: list[Row]) -> Table: @@ -196,66 +302,240 @@ def from_rows(rows: list[Row]) -> Table: Raises ------ - MissingDataError - If an empty list is given. SchemaMismatchError If any of the row schemas does not match with the others. + + Examples + -------- + >>> from safeds.data.tabular.containers import Row, Table + >>> row1 = Row({"a": 1, "b": 2}) + >>> row2 = Row({"a": 3, "b": 4}) + >>> Table.from_rows([row1, row2]) + a b + 0 1 2 + 1 3 4 """ if len(rows) == 0: - raise MissingDataError("This function requires at least one row.") + return Table._from_pandas_dataframe(pd.DataFrame()) schema_compare: Schema = rows[0]._schema - row_array: list[Series] = [] + row_array: list[pd.DataFrame] = [] for row in rows: if schema_compare != row._schema: raise SchemaMismatchError row_array.append(row._data) - dataframe: DataFrame = pd.DataFrame(row_array) - dataframe.columns = schema_compare.get_column_names() - return Table(dataframe) + dataframe: DataFrame = pd.concat(row_array, ignore_index=True) + dataframe.columns = schema_compare.column_names + return Table._from_pandas_dataframe(dataframe) + + @staticmethod + def _from_pandas_dataframe(data: pd.DataFrame, schema: Schema | None = None) -> Table: + """ + Create a table from a `pandas.DataFrame`. + + Parameters + ---------- + data : pd.DataFrame + The data. + schema : Schema | None + The schema. If None, the schema is inferred from the data. + + Returns + ------- + table : Table + The created table. + + Examples + -------- + >>> import pandas as pd + >>> from safeds.data.tabular.containers import Table + >>> Table._from_pandas_dataframe(pd.DataFrame({"a": [1], "b": [2]})) + a b + 0 1 2 + """ + data = data.reset_index(drop=True) + + result = object.__new__(Table) + result._data = data + + if schema is None: + # noinspection PyProtectedMember + result._schema = Schema._from_pandas_dataframe(data) + else: + result._schema = schema + if result._data.empty: + result._data = pd.DataFrame(columns=schema.column_names) + + return result # ------------------------------------------------------------------------------------------------------------------ # Dunder methods # ------------------------------------------------------------------------------------------------------------------ - def __init__(self, data: Iterable, schema: Schema | None = None): - self._data: pd.DataFrame = data if isinstance(data, pd.DataFrame) else pd.DataFrame(data) - self._schema: Schema = Schema._from_dataframe(self._data) if schema is None else schema + def __init__(self, data: Mapping[str, Sequence[Any]] | None = None) -> None: + """ + Create a table from a mapping of column names to their values. + + Parameters + ---------- + data : Mapping[str, Sequence[Any]] | None + The data. If None, an empty table is created. + + Raises + ------ + ColumnLengthMismatchError + If columns have different lengths. + + Examples + -------- + >>> from safeds.data.tabular.containers import Table + >>> Table({"a": [1, 2, 3], "b": [4, 5, 6]}) + a b + 0 1 4 + 1 2 5 + 2 3 6 + """ + if data is None: + data = {} - if self._data.empty: - self._data = pd.DataFrame(columns=self._schema.get_column_names()) + # Validation + expected_length: int | None = None + for column_values in data.values(): + if expected_length is None: + expected_length = len(column_values) + elif len(column_values) != expected_length: + raise ColumnLengthMismatchError( + "\n".join(f"{column_name}: {len(column_values)}" for column_name, column_values in data.items()), + ) + # Implementation + self._data: pd.DataFrame = pd.DataFrame(data) self._data = self._data.reset_index(drop=True) - self._data.columns = self._schema.get_column_names() + self._schema: Schema = Schema._from_pandas_dataframe(self._data) def __eq__(self, other: Any) -> bool: + """ + Compare two table instances. + + Returns + ------- + 'True' if contents are equal, 'False' otherwise + + Examples + -------- + >>> from safeds.data.tabular.containers import Row, Table + >>> row1 = Row({"a": 1, "b": 2}) + >>> row2 = Row({"a": 3, "b": 4}) + >>> row3 = Row({"a": 5, "b": 6}) + >>> table1 = Table.from_rows([row1, row2]) + >>> table2 = Table.from_rows([row1, row2]) + >>> table3 = Table.from_rows([row1, row3]) + >>> table1 == table2 + True + >>> table1 == table3 + False + """ if not isinstance(other, Table): return NotImplemented if self is other: return True + if self.number_of_columns == 0 and other.number_of_columns == 0: + return True table1 = self.sort_columns() table2 = other.sort_columns() - return table1._data.equals(table2._data) and table1._schema == table2._schema - - def __hash__(self) -> int: - return hash(self._data) + if table1.number_of_rows == 0 and table2.number_of_rows == 0: + return table1.column_names == table2.column_names + return table1._schema == table2._schema and table1._data.equals(table2._data) def __repr__(self) -> str: + r""" + Display the table in only one line. + + Returns + ------- + A string representation of the table in only one line. + + Examples + -------- + >>> from safeds.data.tabular.containers import Table + >>> table = Table.from_dict({"a": [1, 3], "b": [2, 4]}) + >>> repr(table) + ' a b\n0 1 2\n1 3 4' + """ tmp = self._data.copy(deep=True) - tmp.columns = self.get_column_names() + tmp.columns = self.column_names return tmp.__repr__() def __str__(self) -> str: tmp = self._data.copy(deep=True) - tmp.columns = self.get_column_names() + tmp.columns = self.column_names return tmp.__str__() # ------------------------------------------------------------------------------------------------------------------ # Properties # ------------------------------------------------------------------------------------------------------------------ + @property + def column_names(self) -> list[str]: + """ + Return a list of all column names in this table. + + Alias for self.schema.column_names -> list[str]. + + Returns + ------- + column_names : list[str] + The list of the column names. + + Examples + -------- + >>> from safeds.data.tabular.containers import Table + >>> table = Table.from_dict({"col1": [1, 3], "col2": [2, 4]}) + >>> table.column_names + ['col1', 'col2'] + """ + return self._schema.column_names + + @property + def number_of_columns(self) -> int: + """ + Return the number of columns. + + Returns + ------- + number_of_columns : int + The number of columns. + + Examples + -------- + >>> from safeds.data.tabular.containers import Table + >>> table = Table.from_dict({"a": [1], "b": [2]}) + >>> table.number_of_columns + 2 + """ + return self._data.shape[1] + + @property + def number_of_rows(self) -> int: + """ + Return the number of rows. + + Returns + ------- + number_of_rows : int + The number of rows. + + Examples + -------- + >>> from safeds.data.tabular.containers import Table + >>> table = Table.from_dict({"a": [1], "b": [2]}) + >>> table.number_of_rows + 1 + """ + return self._data.shape[0] + @property def schema(self) -> Schema: """ @@ -265,6 +545,20 @@ def schema(self) -> Schema: ------- schema : Schema The schema. + + Examples + -------- + >>> from safeds.data.tabular.containers import Row, Table + >>> row = Row({"a": 1, "b": 2.5, "c": "ff"}) + >>> table = Table.from_dict({"a": [1, 8], "b": [2.5, 9], "c": ["g", "g"]}) + >>> table.schema + Schema({ + 'a': Integer, + 'b': RealNumber, + 'c': String + }) + >>> table.schema == row.schema + True """ return self._schema @@ -290,16 +584,21 @@ def get_column(self, column_name: str) -> Column: ------ UnknownColumnNameError If the specified target column name does not exist. + + Examples + -------- + >>> from safeds.data.tabular.containers import Table + >>> table = Table.from_dict({"a": [1], "b": [2]}) + >>> table.get_column("b") + Column('b', [2]) """ - if self._schema.has_column(column_name): - output_column = Column( - column_name, - self._data.iloc[:, [self._schema._get_column_index_by_name(column_name)]].squeeze(), - self._schema.get_type_of_column(column_name), - ) - return output_column + if not self.has_column(column_name): + raise UnknownColumnNameError([column_name]) - raise UnknownColumnNameError([column_name]) + return Column._from_pandas_series( + self._data[column_name], + self.get_column_type(column_name), + ) def has_column(self, column_name: str) -> bool: """ @@ -316,23 +615,19 @@ def has_column(self, column_name: str) -> bool: ------- contains : bool True if the column exists. - """ - return self._schema.has_column(column_name) - def get_column_names(self) -> list[str]: + Examples + -------- + >>> from safeds.data.tabular.containers import Table + >>> table = Table.from_dict({"a": [1], "b": [2]}) + >>> table.has_column("b") + True + >>> table.has_column("c") + False """ - Return a list of all column names in this table. - - Alias for self.schema.get_column_names() -> list[str]. - - Returns - ------- - column_names : list[str] - The list of the column names. - """ - return self._schema.get_column_names() + return self._schema.has_column(column_name) - def get_type_of_column(self, column_name: str) -> ColumnType: + def get_column_type(self, column_name: str) -> ColumnType: """ Return the type of the given column. @@ -350,10 +645,17 @@ def get_type_of_column(self, column_name: str) -> ColumnType: Raises ------ - ColumnNameError + UnknownColumnNameError If the specified target column name does not exist. + + Examples + -------- + >>> from safeds.data.tabular.containers import Table + >>> table = Table.from_dict({"a": [1], "b": [2.5]}) + >>> table.get_column_type("b") + RealNumber """ - return self._schema.get_type_of_column(column_name) + return self._schema.get_column_type(column_name) def get_row(self, index: int) -> Row: """ @@ -373,46 +675,92 @@ def get_row(self, index: int) -> Row: ------ IndexOutOfBoundsError If no row at the specified index exists in this table. + + Examples + -------- + >>> from safeds.data.tabular.containers import Table + >>> table = Table.from_dict({"a": [1, 3], "b": [2, 4]}) + >>> table.get_row(0) + Row({ + 'a': 1, + 'b': 2 + }) """ if len(self._data.index) - 1 < index or index < 0: raise IndexOutOfBoundsError(index) - return Row(self._data.iloc[[index]].squeeze(), self._schema) + + return Row._from_pandas_dataframe(self._data.iloc[[index]], self._schema) # ------------------------------------------------------------------------------------------------------------------ # Information # ------------------------------------------------------------------------------------------------------------------ - def count_rows(self) -> int: - """ - Return the number of rows. - - Returns - ------- - count : int - The number of rows. - """ - return self._data.shape[0] - - def count_columns(self) -> int: - """ - Return the number of columns. - - Returns - ------- - count : int - The number of columns. - """ - return self._data.shape[1] - def summary(self) -> Table: """ Return a table with a number of statistical key values. + This table is not modified. + Returns ------- result : Table The table with statistics. - """ + + Examples + -------- + >>> from safeds.data.tabular.containers import Table + >>> table = Table.from_dict({"a": [1, 3], "b": [2, 4]}) + >>> table.summary() + metrics a b + 0 maximum 3 4 + 1 minimum 1 2 + 2 mean 2.0 3.0 + 3 mode [1, 3] [2, 4] + 4 median 2.0 3.0 + 5 sum 4 6 + 6 variance 2.0 2.0 + 7 standard deviation 1.4142135623730951 1.4142135623730951 + 8 idness 1.0 1.0 + 9 stability 0.5 0.5 + """ + if self.number_of_columns == 0: + return Table( + { + "metrics": [ + "maximum", + "minimum", + "mean", + "mode", + "median", + "sum", + "variance", + "standard deviation", + "idness", + "stability", + ], + }, + ) + elif self.number_of_rows == 0: + table = Table( + { + "metrics": [ + "maximum", + "minimum", + "mean", + "mode", + "median", + "sum", + "variance", + "standard deviation", + "idness", + "stability", + ], + }, + ) + for name in self.column_names: + table = table.add_column(Column(name, ["-", "-", "-", "-", "-", "-", "-", "-", "-", "-"])) + return table + columns = self.to_columns() result = pd.DataFrame() statistics = {} @@ -429,31 +777,48 @@ def summary(self) -> Table: "standard deviation": column.standard_deviation, "idness": column.idness, "stability": column.stability, - "row count": column.count, } values = [] for function in statistics.values(): try: values.append(str(function())) - except NonNumericColumnError: + except (NonNumericColumnError, ValueError): values.append("-") result = pd.concat([result, pd.DataFrame(values)], axis=1) result = pd.concat([pd.DataFrame(list(statistics.keys())), result], axis=1) - result.columns = ["metrics", *self.get_column_names()] + result.columns = ["metrics", *self.column_names] - return Table(result) + return Table._from_pandas_dataframe(result) # ------------------------------------------------------------------------------------------------------------------ # Transformations # ------------------------------------------------------------------------------------------------------------------ + # This method is meant as a way to "cast" instances of subclasses of `Table` to a proper `Table`, dropping any + # additional constraints that might have to hold in the subclass. + # Override accordingly in subclasses. + def _as_table(self: Table) -> Table: + """ + Transform the table to an instance of the Table class. + + The original table is not modified. + + Returns + ------- + table: Table + The table, as an instance of the Table class. + """ + return self + def add_column(self, column: Column) -> Table: """ Return the original table with the provided column attached at the end. + This table is not modified. + Returns ------- result : Table @@ -463,26 +828,36 @@ def add_column(self, column: Column) -> Table: ------ DuplicateColumnNameError If the new column already exists. - ColumnSizeError If the size of the column does not match the amount of rows. + Examples + -------- + >>> from safeds.data.tabular.containers import Table + >>> table = Table.from_dict({"a": [1, 3], "b": [2, 4]}) + >>> col = Column("c", ["d", "e"]) + >>> table.add_column(col) + a b c + 0 1 2 d + 1 3 4 e """ if self.has_column(column.name): raise DuplicateColumnNameError(column.name) - if column._data.size != self.count_rows(): - raise ColumnSizeError(str(self.count_rows()), str(column._data.size)) + if column.number_of_rows != self.number_of_rows and self.number_of_columns != 0: + raise ColumnSizeError(str(self.number_of_rows), str(column._data.size)) result = self._data.copy() - result.columns = self._schema.get_column_names() + result.columns = self._schema.column_names result[column.name] = column._data - return Table(result) + return Table._from_pandas_dataframe(result) def add_columns(self, columns: list[Column] | Table) -> Table: """ Add multiple columns to the table. + This table is not modified. + Parameters ---------- columns : list[Column] or Table @@ -499,25 +874,40 @@ def add_columns(self, columns: list[Column] | Table) -> Table: If at least one of the column sizes from the provided column list does not match the table. DuplicateColumnNameError If at least one column name from the provided column list already exists in the table. + + Examples + -------- + >>> from safeds.data.tabular.containers import Column, Table + >>> table = Table.from_dict({"a": [1, 3], "b": [2, 4]}) + >>> col1 = Column("c", ["d", "e"]) + >>> col2 = Column("d", [3.5, 7.9]) + >>> table.add_columns([col1, col2]) + a b c d + 0 1 2 d 3.5 + 1 3 4 e 7.9 """ if isinstance(columns, Table): columns = columns.to_columns() result = self._data.copy() - result.columns = self._schema.get_column_names() + result.columns = self._schema.column_names for column in columns: if column.name in result.columns: raise DuplicateColumnNameError(column.name) - if column._data.size != self.count_rows(): - raise ColumnSizeError(str(self.count_rows()), str(column._data.size)) + if column.number_of_rows != self.number_of_rows and self.number_of_columns != 0: + raise ColumnSizeError(str(self.number_of_rows), str(column._data.size)) result[column.name] = column._data - return Table(result) + return Table._from_pandas_dataframe(result) def add_row(self, row: Row) -> Table: """ Add a row to the table. + If the table happens to be empty beforehand, respective columns will be added automatically. + + This table is not modified. + Parameters ---------- row : Row @@ -528,21 +918,49 @@ def add_row(self, row: Row) -> Table: table : Table A new table with the added row at the end. - """ - if self._schema != row.schema: + Raises + ------ + SchemaMismatchError + If the schema of the row does not match the table schema. + + Examples + -------- + >>> from safeds.data.tabular.containers import Row, Table + >>> table = Table.from_dict({"a": [1], "b": [2]}) + >>> row = Row.from_dict({"a": 3, "b": 4}) + >>> table.add_row(row) + a b + 0 1 2 + 1 3 4 + """ + int_columns = [] + result = self._copy() + if result.number_of_rows == 0: + int_columns = list(filter(lambda name: isinstance(row[name], int | np.int64), row.column_names)) + if result.number_of_columns == 0: + for column in row.column_names: + result._data[column] = Column(column, []) + result._schema = Schema._from_pandas_dataframe(result._data) + elif result.column_names != row.column_names: + raise SchemaMismatchError + elif result._schema != row.schema: raise SchemaMismatchError - row_frame = row._data.to_frame().T - row_frame.columns = self.get_column_names() + new_df = pd.concat([result._data, row._data]).infer_objects() + new_df.columns = result.column_names + result = Table._from_pandas_dataframe(new_df) + + for column in int_columns: + result = result.replace_column(column, [result.get_column(column).transform(lambda it: int(it))]) - new_df = pd.concat([self._data, row_frame]).infer_objects() - new_df.columns = self.get_column_names() - return Table(new_df) + return result def add_rows(self, rows: list[Row] | Table) -> Table: """ Add multiple rows to a table. + This table is not modified. + Parameters ---------- rows : list[Row] or Table @@ -552,26 +970,57 @@ def add_rows(self, rows: list[Row] | Table) -> Table: ------- result : Table A new table which combines the original table and the given rows. + + Raises + ------ + SchemaMismatchError + If the schema of one of the rows does not match the table schema. + + Examples + -------- + >>> from safeds.data.tabular.containers import Row, Table + >>> table = Table.from_dict({"a": [1], "b": [2]}) + >>> row1 = Row.from_dict({"a": 3, "b": 4}) + >>> row2 = Row.from_dict({"a": 5, "b": 6}) + >>> table.add_rows([row1, row2]) + a b + 0 1 2 + 1 3 4 + 2 5 6 """ if isinstance(rows, Table): rows = rows.to_rows() - result = self._data + int_columns = [] + result = self._copy() for row in rows: - if self._schema != row.schema: + if result.number_of_rows == 0: + int_columns = list(filter(lambda name: isinstance(row[name], int | np.int64), row.column_names)) + if result.number_of_columns == 0: + for column in row.column_names: + result._data[column] = Column(column, []) + result._schema = Schema._from_pandas_dataframe(result._data) + elif result.column_names != row.column_names: + raise SchemaMismatchError + elif result._schema != row.schema: raise SchemaMismatchError - row_frames = [row._data.to_frame().T for row in rows] - for row_frame in row_frames: - row_frame.columns = self.get_column_names() + row_frames = (row._data for row in rows) + + new_df = pd.concat([result._data, *row_frames]).infer_objects() + new_df.columns = result.column_names + result = Table._from_pandas_dataframe(new_df) + + for column in int_columns: + result = result.replace_column(column, [result.get_column(column).transform(lambda it: int(it))]) - result = pd.concat([result, *row_frames]).infer_objects() - result.columns = self.get_column_names() - return Table(result) + return result def filter_rows(self, query: Callable[[Row], bool]) -> Table: """ Return a table with rows filtered by Callable (e.g. lambda function). + This table is not modified. + Parameters ---------- query : lambda function @@ -581,18 +1030,56 @@ def filter_rows(self, query: Callable[[Row], bool]) -> Table: ------- table : Table A table containing only the rows filtered by the query. + + Examples + -------- + >>> from safeds.data.tabular.containers import Table + >>> table = Table.from_dict({"a": [1, 3], "b": [2, 4]}) + >>> table.filter_rows(lambda x: x["a"] < 2) + a b + 0 1 2 """ rows: list[Row] = [row for row in self.to_rows() if query(row)] if len(rows) == 0: - result_table = Table([], self._schema) + result_table = Table._from_pandas_dataframe(pd.DataFrame(), self._schema) else: result_table = self.from_rows(rows) return result_table + _T = TypeVar("_T") + + def group_by(self, key_selector: Callable[[Row], _T]) -> dict[_T, Table]: + """ + Return a dictionary with the output tables as values and the keys from the key_selector. + + This table is not modified. + + Parameters + ---------- + key_selector : Callable[[Row], _T] + A Callable that is applied to all rows and returns the key of the group. + + Returns + ------- + dictionary : dict + A dictionary containing the new tables as values and the selected keys as keys. + """ + dictionary: dict[Table._T, Table] = {} + for row in self.to_rows(): + if key_selector(row) in dictionary: + dictionary[key_selector(row)] = dictionary[key_selector(row)].add_row(row) + else: + dictionary[key_selector(row)] = Table.from_rows([row]) + return dictionary + def keep_only_columns(self, column_names: list[str]) -> Table: """ Return a table with only the given column(s). + This table is not modified. + + Note: When removing the last column of the table, the `number_of_columns` property will be set to 0. + Parameters ---------- column_names : list[str] @@ -605,8 +1092,19 @@ def keep_only_columns(self, column_names: list[str]) -> Table: Raises ------ - ColumnNameError + UnknownColumnNameError If any of the given columns does not exist. + IllegalSchemaModificationError + If removing the columns would violate an invariant in the subclass. + + Examples + -------- + >>> from safeds.data.tabular.containers import Table + >>> table = Table.from_dict({"a": [1, 3], "b": [2, 4]}) + >>> table.keep_only_columns(["b"]) + b + 0 2 + 1 4 """ invalid_columns = [] for name in column_names: @@ -615,14 +1113,18 @@ def keep_only_columns(self, column_names: list[str]) -> Table: if len(invalid_columns) != 0: raise UnknownColumnNameError(invalid_columns) - transformed_data = self._data[column_names] - transformed_data.columns = column_names - return Table(transformed_data) + clone = self._copy() + clone = clone.remove_columns(list(set(self.column_names) - set(column_names))) + return clone def remove_columns(self, column_names: list[str]) -> Table: """ Return a table without the given column(s). + This table is not modified. + + Note: When removing the last column of the table, the `number_of_columns` property will be set to 0. + Parameters ---------- column_names : list[str] @@ -635,8 +1137,19 @@ def remove_columns(self, column_names: list[str]) -> Table: Raises ------ - ColumnNameError + UnknownColumnNameError If any of the given columns does not exist. + IllegalSchemaModificationError + If removing the columns would violate an invariant in the subclass. + + Examples + -------- + >>> from safeds.data.tabular.containers import Table + >>> table = Table.from_dict({"a": [1, 3], "b": [2, 4]}) + >>> table.remove_columns(["b"]) + a + 0 1 + 1 3 """ invalid_columns = [] for name in column_names: @@ -646,17 +1159,39 @@ def remove_columns(self, column_names: list[str]) -> Table: raise UnknownColumnNameError(invalid_columns) transformed_data = self._data.drop(labels=column_names, axis="columns") - transformed_data.columns = [name for name in self._schema.get_column_names() if name not in column_names] - return Table(transformed_data) + transformed_data.columns = [name for name in self._schema.column_names if name not in column_names] + + if len(transformed_data.columns) == 0: + return Table() + + return Table._from_pandas_dataframe(transformed_data) def remove_columns_with_missing_values(self) -> Table: """ Return a table without the columns that contain missing values. + This table is not modified. + + Note: When removing the last column of the table, the `number_of_columns` property will be set to 0. + Returns ------- table : Table A table without the columns that contain missing values. + + Raises + ------ + IllegalSchemaModificationError + If removing the columns would violate an invariant in the subclass. + + Examples + -------- + >>> from safeds.data.tabular.containers import Table + >>> table = Table.from_dict({"a": [1, 2], "b": [None, 2]}) + >>> table.remove_columns_with_missing_values() + a + 0 1 + 1 2 """ return Table.from_columns([column for column in self.to_columns() if not column.has_missing_values()]) @@ -664,11 +1199,28 @@ def remove_columns_with_non_numerical_values(self) -> Table: """ Return a table without the columns that contain non-numerical values. + This table is not modified. + + Note: When removing the last column of the table, the `number_of_columns` property will be set to 0. + Returns ------- table : Table A table without the columns that contain non-numerical values. + Raises + ------ + IllegalSchemaModificationError + If removing the columns would violate an invariant in the subclass. + + Examples + -------- + >>> from safeds.data.tabular.containers import Table + >>> table = Table.from_dict({"a": [1, 0], "b": ["test", 2]}) + >>> table.remove_columns_with_non_numerical_values() + a + 0 1 + 1 0 """ return Table.from_columns([column for column in self.to_columns() if column.type.is_numeric()]) @@ -676,27 +1228,48 @@ def remove_duplicate_rows(self) -> Table: """ Return a copy of the table with every duplicate row removed. + This table is not modified. + Returns ------- result : Table The table with the duplicate rows removed. + + Examples + -------- + >>> from safeds.data.tabular.containers import Table + >>> table = Table.from_dict({"a": [1, 3, 3], "b": [2, 4, 4]}) + >>> table.remove_duplicate_rows() + a b + 0 1 2 + 1 3 4 """ result = self._data.drop_duplicates(ignore_index=True) - result.columns = self._schema.get_column_names() - return Table(result) + result.columns = self._schema.column_names + return Table._from_pandas_dataframe(result) def remove_rows_with_missing_values(self) -> Table: """ Return a table without the rows that contain missing values. + This table is not modified. + Returns ------- table : Table A table without the rows that contain missing values. + + Examples + -------- + >>> from safeds.data.tabular.containers import Table + >>> table = Table.from_dict({"a": [1.0, None, 3], "b": [2, 4.0, None]}) + >>> table.remove_rows_with_missing_values() + a b + 0 1.0 2.0 """ result = self._data.copy(deep=True) result = result.dropna(axis="index") - return Table(result, self._schema) + return Table._from_pandas_dataframe(result, self._schema) def remove_rows_with_outliers(self) -> Table: """ @@ -706,10 +1279,34 @@ def remove_rows_with_outliers(self) -> Table: Missing values are not considered outliers. They are also ignored during the calculation of the standard deviation. + This table is not modified. + Returns ------- new_table : Table A new table without rows containing outliers. + + Examples + -------- + >>> from safeds.data.tabular.containers import Column, Table + >>> c1 = Column("a", [1, 3, 1, 0.1, 0, 0, 0, 0, 0, 0, 0, 0]) + >>> c2 = Column("b", [1.5, 1, 0.5, 0.01, 0, 0, 0, 0, 0, 0, 0, 0]) + >>> c3 = Column("c", [0.1, 0.00, 0.4, 0.2, 0, 0, 0, 0, 0, 0, 0, 0]) + >>> c4 = Column("d", [-1000000, 1000000, -1000000, -1000000, -1000000, -1000000, -1000000, -1000000, -1000000, -1000000, -1000000, -1000000]) + >>> table = Table.from_columns([c1, c2, c3, c4]) + >>> table.remove_rows_with_outliers() + a b c d + 0 1.0 1.50 0.1 -1000000 + 1 1.0 0.50 0.4 -1000000 + 2 0.1 0.01 0.2 -1000000 + 3 0.0 0.00 0.0 -1000000 + 4 0.0 0.00 0.0 -1000000 + 5 0.0 0.00 0.0 -1000000 + 6 0.0 0.00 0.0 -1000000 + 7 0.0 0.00 0.0 -1000000 + 8 0.0 0.00 0.0 -1000000 + 9 0.0 0.00 0.0 -1000000 + 10 0.0 0.00 0.0 -1000000 """ copy = self._data.copy(deep=True) @@ -717,12 +1314,14 @@ def remove_rows_with_outliers(self) -> Table: z_scores = np.absolute(stats.zscore(table_without_nonnumericals._data, nan_policy="omit")) filter_ = ((z_scores < 3) | np.isnan(z_scores)).all(axis=1) - return Table(copy[filter_], self._schema) + return Table._from_pandas_dataframe(copy[filter_], self._schema) def rename_column(self, old_name: str, new_name: str) -> Table: """ Rename a single column. + This table is not modified. + Parameters ---------- old_name : str @@ -737,83 +1336,115 @@ def rename_column(self, old_name: str, new_name: str) -> Table: Raises ------ - ColumnNameError + UnknownColumnNameError If the specified old target column name does not exist. DuplicateColumnNameError If the specified new target column name already exists. + + Examples + -------- + >>> from safeds.data.tabular.containers import Table + >>> table = Table.from_dict({"a": [1], "b": [2]}) + >>> table.rename_column("b", "c") + a c + 0 1 2 """ - if old_name not in self._schema.get_column_names(): + if old_name not in self._schema.column_names: raise UnknownColumnNameError([old_name]) if old_name == new_name: return self - if new_name in self._schema.get_column_names(): + if new_name in self._schema.column_names: raise DuplicateColumnNameError(new_name) new_df = self._data.copy() - new_df.columns = self._schema.get_column_names() - return Table(new_df.rename(columns={old_name: new_name})) + new_df.columns = self._schema.column_names + return Table._from_pandas_dataframe(new_df.rename(columns={old_name: new_name})) - def replace_column(self, old_column_name: str, new_column: Column) -> Table: + def replace_column(self, old_column_name: str, new_columns: list[Column]) -> Table: """ - Return a copy of the table with the specified old column replaced by a new column. Keeps the order of columns. + Return a copy of the table with the specified old column replaced by a list of new columns. + + The order of columns is kept. + + This table is not modified. Parameters ---------- old_column_name : str The name of the column to be replaced. - new_column : Column - The new column replacing the old column. + new_columns : list[Column] + The list of new columns replacing the old column. Returns ------- result : Table - A table with the old column replaced by the new column. + A table with the old column replaced by the new columns. Raises ------ UnknownColumnNameError If the old column does not exist. - DuplicateColumnNameError - If the new column already exists and the existing column is not affected by the replacement. - + If at least one of the new columns already exists and the existing column is not affected by the replacement. ColumnSizeError - If the size of the column does not match the amount of rows. - """ - if old_column_name not in self._schema.get_column_names(): + If the size of at least one of the new columns does not match the amount of rows. + IllegalSchemaModificationError + If replacing the column would violate an invariant in the subclass. + + Examples + -------- + >>> from safeds.data.tabular.containers import Column, Table + >>> table = Table.from_dict({"a": [1], "b": [2]}) + >>> new_col = Column("new", [3]) + >>> table.replace_column("b", [new_col]) + a new + 0 1 3 + """ + if old_column_name not in self._schema.column_names: raise UnknownColumnNameError([old_column_name]) - if new_column.name in self._schema.get_column_names() and new_column.name != old_column_name: - raise DuplicateColumnNameError(new_column.name) + columns = list[Column]() + for old_column in self.column_names: + if old_column == old_column_name: + for new_column in new_columns: + if new_column.name in self.column_names and new_column.name != old_column_name: + raise DuplicateColumnNameError(new_column.name) - if self.count_rows() != new_column._data.size: - raise ColumnSizeError(str(self.count_rows()), str(new_column._data.size)) + if self.number_of_rows != new_column.number_of_rows: + raise ColumnSizeError(str(self.number_of_rows), str(new_column.number_of_rows)) + columns.append(new_column) + else: + columns.append(self.get_column(old_column)) - if old_column_name != new_column.name: - renamed_table = self.rename_column(old_column_name, new_column.name) - result = renamed_table._data - result.columns = renamed_table._schema.get_column_names() - else: - result = self._data.copy() - result.columns = self._schema.get_column_names() - - result[new_column.name] = new_column._data - return Table(result) + return Table.from_columns(columns) def shuffle_rows(self) -> Table: """ Shuffle the table randomly. + This table is not modified. + Returns ------- result : Table The shuffled Table. + Examples + -------- + >>> from safeds.data.tabular.containers import Table + >>> import numpy as np + >>> np.random.seed(123456) + >>> table = Table.from_dict({"a": [1, 3, 5], "b": [2, 4, 6]}) + >>> table.shuffle_rows() + a b + 0 5 6 + 1 1 2 + 2 3 4 """ new_df = self._data.sample(frac=1.0) - new_df.columns = self._schema.get_column_names() - return Table(new_df) + new_df.columns = self._schema.column_names + return Table._from_pandas_dataframe(new_df) def slice_rows( self, @@ -824,6 +1455,8 @@ def slice_rows( """ Slice a part of the table into a new table. + This table is not modified. + Parameters ---------- start : int @@ -840,21 +1473,32 @@ def slice_rows( Raises ------ - ValueError + IndexOutOfBoundsError If the index is out of bounds. + + Examples + -------- + >>> from safeds.data.tabular.containers import Table + >>> table = Table.from_dict({"a": [1, 3, 5], "b": [2, 4, 6]}) + >>> table.slice_rows(0, 2) + a b + 0 1 2 + 1 3 4 """ if start is None: start = 0 if end is None: - end = self.count_rows() + end = self.number_of_rows - if start < 0 or end < 0 or start >= self.count_rows() or end > self.count_rows() or end < start: - raise ValueError("The given index is out of bounds") + if end < start: + raise IndexOutOfBoundsError(slice(start, end)) + if start < 0 or end < 0 or start > self.number_of_rows or end > self.number_of_rows: + raise IndexOutOfBoundsError(start if start < 0 or start > self.number_of_rows else end) new_df = self._data.iloc[start:end:step] - new_df.columns = self._schema.get_column_names() - return Table(new_df) + new_df.columns = self._schema.column_names + return Table._from_pandas_dataframe(new_df) def sort_columns( self, @@ -864,7 +1508,7 @@ def sort_columns( """ Sort the columns of a `Table` with the given comparator and return a new `Table`. - The original table is not modified. The comparator is a function that takes two columns `col1` and `col2` and + The comparator is a function that takes two columns `col1` and `col2` and returns an integer: * If `col1` should be ordered before `col2`, the function should return a negative number. @@ -873,6 +1517,8 @@ def sort_columns( If no comparator is given, the columns will be sorted alphabetically by their name. + This table is not modified. + Parameters ---------- comparator : Callable[[Column, Column], int] @@ -882,6 +1528,21 @@ def sort_columns( ------- new_table : Table A new table with sorted columns. + + Examples + -------- + >>> from safeds.data.tabular.containers import Table + >>> table = Table.from_dict({"a": [1], "b": [2] }) + >>> table.sort_columns(lambda col1, col2: 1) + a b + 0 1 2 + >>> table.sort_columns(lambda col1, col2: -1) + b a + 0 2 1 + >>> table2 = Table.from_dict({"b": [2], "a": [1]}) + >>> table2.sort_columns() + a b + 0 1 2 """ columns = self.to_columns() columns.sort(key=functools.cmp_to_key(comparator)) @@ -891,13 +1552,15 @@ def sort_rows(self, comparator: Callable[[Row, Row], int]) -> Table: """ Sort the rows of a `Table` with the given comparator and return a new `Table`. - The original table is not modified. The comparator is a function that takes two rows `row1` and `row2` and + The comparator is a function that takes two rows `row1` and `row2` and returns an integer: * If `row1` should be ordered before `row2`, the function should return a negative number. * If `row1` should be ordered after `row2`, the function should return a positive number. * If the original order of `row1` and `row2` should be kept, the function should return 0. + This table is not modified. + Parameters ---------- comparator : Callable[[Row, Row], int] @@ -907,6 +1570,26 @@ def sort_rows(self, comparator: Callable[[Row, Row], int]) -> Table: ------- new_table : Table A new table with sorted rows. + + Examples + -------- + >>> from safeds.data.tabular.containers import Table + >>> table = Table.from_dict({"a": [1, 3, 5], "b": [2, 4, 6] }) + >>> table.sort_rows(lambda row1, row2: 1) + a b + 0 1 2 + 1 3 4 + 2 5 6 + >>> table.sort_rows(lambda row1, row2: -1) + a b + 0 5 6 + 1 3 4 + 2 1 2 + >>> table.sort_rows(lambda row1, row2: 0) + a b + 0 1 2 + 1 3 4 + 2 5 6 """ rows = self.to_rows() rows.sort(key=functools.cmp_to_key(comparator)) @@ -916,10 +1599,12 @@ def split(self, percentage_in_first: float) -> tuple[Table, Table]: """ Split the table into two new tables. + This table is not modified. + Parameters ---------- percentage_in_first : float - The desired size of the first table in percentage to the given table. + The desired size of the first table in percentage to the given table; must be between 0 and 1. Returns ------- @@ -927,39 +1612,76 @@ def split(self, percentage_in_first: float) -> tuple[Table, Table]: A tuple containing the two resulting tables. The first table has the specified size, the second table contains the rest of the data. - - """ - if percentage_in_first <= 0 or percentage_in_first >= 1: - raise ValueError("the given percentage is not in range") + Raises + ------ + ValueError: + if the 'percentage_in_first' is not between 0 and 1 + + Examples + -------- + >>> from safeds.data.tabular.containers import Table + >>> table = Table.from_dict({"temperature": [10, 15, 20, 25, 30], "sales": [54, 74, 90, 206, 210]}) + >>> slices = table.split(0.4) + >>> slices[0] + temperature sales + 0 10 54 + 1 15 74 + >>> slices[1] + temperature sales + 0 20 90 + 1 25 206 + 2 30 210 + """ + if percentage_in_first < 0 or percentage_in_first > 1: + raise ValueError("The given percentage is not between 0 and 1") + if self.number_of_rows == 0: + return Table(), Table() return ( - self.slice_rows(0, round(percentage_in_first * self.count_rows())), - self.slice_rows(round(percentage_in_first * self.count_rows())), + self.slice_rows(0, round(percentage_in_first * self.number_of_rows)), + self.slice_rows(round(percentage_in_first * self.number_of_rows)), ) def tag_columns(self, target_name: str, feature_names: list[str] | None = None) -> TaggedTable: """ Mark the columns of the table as target column or feature columns. The original table is not modified. + This table is not modified. + Parameters ---------- target_name : str Name of the target column. - feature_names : Optional[list[str]] + feature_names : list[str] | None Names of the feature columns. If None, all columns except the target column are used. Returns ------- tagged_table : TaggedTable A new tagged table with the given target and feature names. + + Raises + ------ + ValueError + If the target column is also a feature column. + ValueError + If no feature columns are specified. + + Examples + -------- + >>> from safeds.data.tabular.containers import Table, TaggedTable + >>> table = Table.from_dict({"item": ["apple", "milk", "beer"], "price": [1.10, 1.19, 1.79], "amount_bought": [74, 72, 51]}) + >>> tagged_table = table.tag_columns(target_name="amount_bought", feature_names=["item", "price"]) """ from ._tagged_table import TaggedTable - return TaggedTable(self._data, target_name, feature_names, self._schema) + return TaggedTable._from_table(self, target_name, feature_names) def transform_column(self, name: str, transformer: Callable[[Row], Any]) -> Table: """ Transform provided column by calling provided transformer. + This table is not modified. + Returns ------- result : Table @@ -970,18 +1692,107 @@ def transform_column(self, name: str, transformer: Callable[[Row], Any]) -> Tabl UnknownColumnNameError If the column does not exist. + Examples + -------- + >>> from safeds.data.tabular.containers import Table + >>> table = Table.from_dict({"item": ["apple", "milk", "beer"], "price": [1.00, 1.19, 1.79]}) + >>> table.transform_column("price", lambda row: row.get_value("price") * 100) + item price + 0 apple 100.0 + 1 milk 119.0 + 2 beer 179.0 """ if self.has_column(name): items: list = [transformer(item) for item in self.to_rows()] - result: Column = Column(name, pd.Series(items)) + result: list[Column] = [Column(name, items)] return self.replace_column(name, result) raise UnknownColumnNameError([name]) + def transform_table(self, transformer: TableTransformer) -> Table: + """ + Apply a learned transformation onto this table. + + This table is not modified. + + Parameters + ---------- + transformer : TableTransformer + The transformer which transforms the given table. + + Returns + ------- + transformed_table : Table + The transformed table. + + Raises + ------ + TransformerNotFittedError + If the transformer has not been fitted yet. + IllegalSchemaModificationError + If replacing the column would violate an invariant in the subclass. + + Examples + -------- + >>> from safeds.data.tabular.transformation import OneHotEncoder + >>> from safeds.data.tabular.containers import Table + >>> transformer = OneHotEncoder() + >>> table = Table.from_dict({"fruit": ["apple", "pear", "apple"], "pet": ["dog", "duck", "duck"]}) + >>> transformer = transformer.fit(table, None) + >>> table.transform_table(transformer) + fruit__apple fruit__pear pet__dog pet__duck + 0 1.0 0.0 1.0 0.0 + 1 0.0 1.0 0.0 1.0 + 2 1.0 0.0 0.0 1.0 + """ + return transformer.transform(self) + + def inverse_transform_table(self, transformer: InvertibleTableTransformer) -> Table: + """ + Invert the transformation applied by the given transformer. + + This table is not modified. + + Parameters + ---------- + transformer : InvertibleTableTransformer + A transformer that was fitted with columns, which are all present in the table. + + Returns + ------- + table : Table + The original table. + + Raises + ------ + TransformerNotFittedError + If the transformer has not been fitted yet. + + Examples + -------- + >>> from safeds.data.tabular.transformation import OneHotEncoder + >>> from safeds.data.tabular.containers import Table + >>> transformer = OneHotEncoder() + >>> table = Table.from_dict({"a": ["j", "k", "k"], "b": ["x", "y", "x"]}) + >>> transformer = transformer.fit(table, None) + >>> transformed_table = transformer.transform(table) + >>> transformed_table.inverse_transform_table(transformer) + a b + 0 j x + 1 k y + 2 k x + >>> transformer.inverse_transform(transformed_table) + a b + 0 j x + 1 k y + 2 k x + """ + return transformer.inverse_transform(self) + # ------------------------------------------------------------------------------------------------------------------ # Plotting # ------------------------------------------------------------------------------------------------------------------ - def correlation_heatmap(self) -> Image: + def plot_correlation_heatmap(self) -> Image: """ Plot a correlation heatmap for all numerical columns of this `Table`. @@ -989,6 +1800,12 @@ def correlation_heatmap(self) -> Image: ------- plot: Image The plot as an image. + + Examples + -------- + >>> from safeds.data.tabular.containers import Table + >>> table = Table.from_dict({"temperature": [10, 15, 20, 25, 30], "sales": [54, 74, 90, 206, 210]}) + >>> image = table.plot_correlation_heatmap() """ only_numerical = self.remove_columns_with_non_numerical_values() @@ -997,8 +1814,8 @@ def correlation_heatmap(self) -> Image: data=only_numerical._data.corr(), vmin=-1, vmax=1, - xticklabels=only_numerical.get_column_names(), - yticklabels=only_numerical.get_column_names(), + xticklabels=only_numerical.column_names, + yticklabels=only_numerical.column_names, cmap="vlag", ) plt.tight_layout() @@ -1009,7 +1826,7 @@ def correlation_heatmap(self) -> Image: buffer.seek(0) return Image(buffer, format_=ImageFormat.PNG) - def lineplot(self, x_column_name: str, y_column_name: str) -> Image: + def plot_lineplot(self, x_column_name: str, y_column_name: str) -> Image: """ Plot two columns against each other in a lineplot. @@ -1032,11 +1849,18 @@ def lineplot(self, x_column_name: str, y_column_name: str) -> Image: ------ UnknownColumnNameError If either of the columns do not exist. + + Examples + -------- + >>> from safeds.data.tabular.containers import Table + >>> table = Table.from_dict({"temperature": [10, 15, 20, 25, 30], "sales": [54, 74, 90, 206, 210]}) + >>> image = table.plot_lineplot("temperature", "sales") """ - if not self.has_column(x_column_name): - raise UnknownColumnNameError([x_column_name]) - if not self.has_column(y_column_name): - raise UnknownColumnNameError([y_column_name]) + if not self.has_column(x_column_name) or not self.has_column(y_column_name): + raise UnknownColumnNameError( + ([x_column_name] if not self.has_column(x_column_name) else []) + + ([y_column_name] if not self.has_column(y_column_name) else []), + ) fig = plt.figure() ax = sns.lineplot( @@ -1059,7 +1883,7 @@ def lineplot(self, x_column_name: str, y_column_name: str) -> Image: buffer.seek(0) return Image(buffer, format_=ImageFormat.PNG) - def scatterplot(self, x_column_name: str, y_column_name: str) -> Image: + def plot_scatterplot(self, x_column_name: str, y_column_name: str) -> Image: """ Plot two columns against each other in a scatterplot. @@ -1079,11 +1903,18 @@ def scatterplot(self, x_column_name: str, y_column_name: str) -> Image: ------ UnknownColumnNameError If either of the columns do not exist. + + Examples + -------- + >>> from safeds.data.tabular.containers import Table + >>> table = Table.from_dict({"temperature": [10, 15, 20, 25, 30], "sales": [54, 74, 90, 206, 210]}) + >>> image = table.plot_scatterplot("temperature", "sales") """ - if not self.has_column(x_column_name): - raise UnknownColumnNameError([x_column_name]) - if not self.has_column(y_column_name): - raise UnknownColumnNameError([y_column_name]) + if not self.has_column(x_column_name) or not self.has_column(y_column_name): + raise UnknownColumnNameError( + ([x_column_name] if not self.has_column(x_column_name) else []) + + ([y_column_name] if not self.has_column(y_column_name) else []), + ) fig = plt.figure() ax = sns.scatterplot( @@ -1106,11 +1937,93 @@ def scatterplot(self, x_column_name: str, y_column_name: str) -> Image: buffer.seek(0) return Image(buffer, format_=ImageFormat.PNG) + def plot_boxplots(self) -> Image: + """ + Plot a boxplot for every numerical column. + + Returns + ------- + plot: Image + The plot as an image. + + Raises + ------ + NonNumericColumnError + If the table contains only non-numerical columns. + + Examples + -------- + >>> from safeds.data.tabular.containers import Table + >>> table = Table({"a":[1, 2], "b": [3, 42]}) + >>> image = table.plot_boxplots() + """ + numerical_table = self.remove_columns_with_non_numerical_values() + if numerical_table.number_of_columns == 0: + raise NonNumericColumnError("This table contains only non-numerical columns.") + col_wrap = min(numerical_table.number_of_columns, 3) + + data = pd.melt(numerical_table._data, value_vars=numerical_table.column_names) + grid = sns.FacetGrid(data, col="variable", col_wrap=col_wrap, sharex=False, sharey=False) + with warnings.catch_warnings(): + warnings.filterwarnings( + "ignore", + message="Using the boxplot function without specifying `order` is likely to produce an incorrect plot.", + ) + grid.map(sns.boxplot, "variable", "value") + grid.set_xlabels("") + grid.set_ylabels("") + grid.set_titles("{col_name}") + for axes in grid.axes.flat: + axes.set_xticks([]) + plt.tight_layout() + fig = grid.fig + + buffer = io.BytesIO() + fig.savefig(buffer, format="png") + plt.close() # Prevents the figure from being displayed directly + buffer.seek(0) + return Image(buffer, format_=ImageFormat.PNG) + + def plot_histograms(self) -> Image: + """ + Plot a histogram for every column. + + Returns + ------- + plot: Image + The plot as an image. + + Examples + -------- + >>> from safeds.data.tabular.containers import Table + >>> table = Table({"a": [2, 3, 5, 1], "b": [54, 74, 90, 2014]}) + >>> image = table.plot_histograms() + """ + col_wrap = min(self.number_of_columns, 3) + + data = pd.melt(self._data, value_vars=self.column_names) + grid = sns.FacetGrid(data=data, col="variable", col_wrap=col_wrap, sharex=False, sharey=False) + grid.map(sns.histplot, "value") + grid.set_xlabels("") + grid.set_ylabels("") + grid.set_titles("{col_name}") + for axes in grid.axes.flat: + axes.set_xticks(axes.get_xticks()) + axes.set_xticklabels(axes.get_xticklabels(), rotation=45, horizontalalignment="right") + grid.tight_layout() + fig = grid.fig + + buffer = io.BytesIO() + fig.savefig(buffer, format="png") + plt.close() + buffer.seek(0) + return Image(buffer, ImageFormat.PNG) + # ------------------------------------------------------------------------------------------------------------------ # Conversion # ------------------------------------------------------------------------------------------------------------------ - def to_csv_file(self, path: str) -> None: + def to_csv_file(self, path: str | Path) -> None: """ Write the data from the table into a CSV file. @@ -1119,15 +2032,67 @@ def to_csv_file(self, path: str) -> None: Parameters ---------- - path : str + path : str | Path The path to the output file. - """ - Path(path).parent.mkdir(parents=True, exist_ok=True) + + Raises + ------ + WrongFileExtensionError + If the file is not a csv file. + + Examples + -------- + >>> from safeds.data.tabular.containers import Table + >>> table = Table.from_dict({"a": [1, 2, 3], "b": [4, 5, 6]}) + >>> table.to_csv_file("./src/resources/to_csv_file.csv") + """ + path = Path(path) + if path.suffix != ".csv": + raise WrongFileExtensionError(path, ".csv") + path.parent.mkdir(parents=True, exist_ok=True) data_to_csv = self._data.copy() - data_to_csv.columns = self._schema.get_column_names() + data_to_csv.columns = self._schema.column_names data_to_csv.to_csv(path, index=False) - def to_json_file(self, path: str) -> None: + def to_excel_file(self, path: str | Path) -> None: + """ + Write the data from the table into an Excel file. + + Valid file extensions are `.xls`, '.xlsx', `.xlsm`, `.xlsb`, `.odf`, `.ods` and `.odt`. + If the file and/or the directories do not exist, they will be created. If the file already exists, it will be + overwritten. + + Parameters + ---------- + path : str | Path + The path to the output file. + + Raises + ------ + WrongFileExtensionError + If the file is not an Excel file. + + Examples + -------- + >>> from safeds.data.tabular.containers import Table + >>> table = Table.from_dict({"a": [1, 2, 3], "b": [4, 5, 6]}) + >>> table.to_excel_file("./src/resources/to_excel_file.xlsx") + """ + path = Path(path) + excel_extensions = [".xls", ".xlsx", ".xlsm", ".xlsb", ".odf", ".ods", ".odt"] + if path.suffix not in excel_extensions: + raise WrongFileExtensionError(path, excel_extensions) + + # Create Excel metadata in the file + tmp_table_file = openpyxl.Workbook() + tmp_table_file.save(path) + + path.parent.mkdir(parents=True, exist_ok=True) + data_to_excel = self._data.copy() + data_to_excel.columns = self._schema.column_names + data_to_excel.to_excel(path) + + def to_json_file(self, path: str | Path) -> None: """ Write the data from the table into a JSON file. @@ -1136,12 +2101,26 @@ def to_json_file(self, path: str) -> None: Parameters ---------- - path : str + path : str | Path The path to the output file. - """ - Path(path).parent.mkdir(parents=True, exist_ok=True) + + Raises + ------ + WrongFileExtensionError + If the file is not a JSON file. + + Examples + -------- + >>> from safeds.data.tabular.containers import Table + >>> table = Table.from_dict({"a": [1, 2, 3], "b": [4, 5, 6]}) + >>> table.to_json_file("./src/resources/to_json_file.json") + """ + path = Path(path) + if path.suffix != ".json": + raise WrongFileExtensionError(path, ".json") + path.parent.mkdir(parents=True, exist_ok=True) data_to_json = self._data.copy() - data_to_json.columns = self._schema.get_column_names() + data_to_json.columns = self._schema.column_names data_to_json.to_json(path) def to_dict(self) -> dict[str, list[Any]]: @@ -1152,8 +2131,35 @@ def to_dict(self) -> dict[str, list[Any]]: ------- data : dict[str, list[Any]] Dictionary representation of the table. + + Examples + -------- + >>> from safeds.data.tabular.containers import Table + >>> row1 = Row({"a": 1, "b": 5}) + >>> row2 = Row({"a": 2, "b": 6}) + >>> table1 = Table.from_rows([row1, row2]) + >>> table2 = Table.from_dict({"a": [1, 2], "b": [5, 6]}) + >>> table1 == table2 + True + """ + return {column_name: list(self.get_column(column_name)) for column_name in self.column_names} + + def to_html(self) -> str: """ - return {column_name: list(self.get_column(column_name)) for column_name in self.get_column_names()} + Return an HTML representation of the table. + + Returns + ------- + output : str + The generated HTML. + + Examples + -------- + >>> from safeds.data.tabular.containers import Table + >>> table = Table({"a": [1, 2, 3], "b": [4, 5, 6]}) + >>> html = table.to_html() + """ + return self._data.to_html(max_rows=self._data.shape[0], max_cols=self._data.shape[1]) def to_columns(self) -> list[Column]: """ @@ -1163,8 +2169,15 @@ def to_columns(self) -> list[Column]: ------- columns : list[Columns] List of columns. + + Examples + -------- + >>> from safeds.data.tabular.containers import Table + >>> table = Table.from_dict({"a":[1, 2],"b":[20, 30]}) + >>> table.to_columns() + [Column('a', [1, 2]), Column('b', [20, 30])] """ - return [self.get_column(name) for name in self._schema.get_column_names()] + return [self.get_column(name) for name in self._schema.column_names] def to_rows(self) -> list[Row]: """ @@ -1174,27 +2187,42 @@ def to_rows(self) -> list[Row]: ------- rows : list[Row] List of rows. - """ - return [Row(series_row, self._schema) for (_, series_row) in self._data.iterrows()] + + Examples + -------- + >>> from safeds.data.tabular.containers import Table + >>> table = Table.from_dict({"a":[1, 2],"b":[20, 30]}) + >>> table.to_rows() + [Row({ + 'a': 1, + 'b': 20 + }), Row({ + 'a': 2, + 'b': 30 + })] + """ + return [ + Row._from_pandas_dataframe( + pd.DataFrame([list(series_row)], columns=self._schema.column_names), + self._schema, + ) + for (_, series_row) in self._data.iterrows() + ] # ------------------------------------------------------------------------------------------------------------------ # IPython integration # ------------------------------------------------------------------------------------------------------------------ - def _ipython_display_(self) -> DisplayHandle: + def _repr_html_(self) -> str: """ - Return a display object for the column to be used in Jupyter Notebooks. + Return an HTML representation of the table. Returns ------- - output : DisplayHandle - Output object. + output : str + The generated HTML. """ - tmp = self._data.copy(deep=True) - tmp.columns = self.get_column_names() - - with pd.option_context("display.max_rows", tmp.shape[0], "display.max_columns", tmp.shape[1]): - return display(tmp) + return self._data.to_html(max_rows=self._data.shape[0], max_cols=self._data.shape[1], notebook=True) # ------------------------------------------------------------------------------------------------------------------ # Dataframe interchange protocol @@ -1209,6 +2237,9 @@ def __dataframe__(self, nan_as_null: bool = False, allow_copy: bool = True): # decide to call this method, you should not rely on any capabilities of the returned object beyond the dataframe interchange protocol. + The specification of the dataframe interchange protocol can be found on + [GitHub](https://github.com/data-apis/dataframe-api). + Parameters ---------- nan_as_null : bool @@ -1220,15 +2251,25 @@ def __dataframe__(self, nan_as_null: bool = False, allow_copy: bool = True): # ------- dataframe A DataFrame object that conforms to the dataframe interchange protocol. - - Notes - ----- - The specification of the dataframe interchange protocol can be found at - https://github.com/data-apis/dataframe-api. """ if not allow_copy: raise NotImplementedError("For the moment we need to copy the data, so `allow_copy` must be True.") data_copy = self._data.copy() - data_copy.columns = self.get_column_names() + data_copy.columns = self.column_names return data_copy.__dataframe__(nan_as_null, allow_copy) + + # ------------------------------------------------------------------------------------------------------------------ + # Helpers + # ------------------------------------------------------------------------------------------------------------------ + + def _copy(self) -> Table: + """ + Return a copy of this table. + + Returns + ------- + table : Table + The copy of this table. + """ + return copy.deepcopy(self) diff --git a/src/safeds/data/tabular/containers/_tagged_table.py b/src/safeds/data/tabular/containers/_tagged_table.py index 959f59856..0f3c6e674 100644 --- a/src/safeds/data/tabular/containers/_tagged_table.py +++ b/src/safeds/data/tabular/containers/_tagged_table.py @@ -1,9 +1,14 @@ -from collections.abc import Iterable +from __future__ import annotations -from IPython.core.display_functions import DisplayHandle +import copy +from typing import TYPE_CHECKING -from safeds.data.tabular.containers import Column, Table -from safeds.data.tabular.typing import Schema +from safeds.data.tabular.containers import Column, Row, Table +from safeds.exceptions import ColumnIsTargetError, IllegalSchemaModificationError, UnknownColumnNameError + +if TYPE_CHECKING: + from collections.abc import Callable, Mapping, Sequence + from typing import Any class TaggedTable(Table): @@ -12,32 +17,138 @@ class TaggedTable(Table): Parameters ---------- - data : Iterable + data : Mapping[str, Sequence[Any]] The data. target_name : str Name of the target column. - feature_names : Optional[list[str]] + feature_names : list[str] | None Names of the feature columns. If None, all columns except the target column are used. - schema : Optional[Schema] - The schema of the table. If not specified, the schema will be inferred from the data. + + Raises + ------ + ColumnLengthMismatchError + If columns have different lengths. + ValueError + If the target column is also a feature column. + ValueError + If no feature columns are specified. + + Examples + -------- + >>> from safeds.data.tabular.containers import Table, TaggedTable + >>> table = Table({"col1": ["a", "b"], "col2": [1, 2]}) + >>> tagged_table = table.tag_columns("col2", ["col1"]) """ + # ------------------------------------------------------------------------------------------------------------------ + # Creation + # ------------------------------------------------------------------------------------------------------------------ + + @staticmethod + def _from_table( + table: Table, + target_name: str, + feature_names: list[str] | None = None, + ) -> TaggedTable: + """ + Create a tagged table from a table. + + Parameters + ---------- + table : Table + The table. + target_name : str + Name of the target column. + feature_names : list[str] | None + Names of the feature columns. If None, all columns except the target column are used. + + Returns + ------- + tagged_table : TaggedTable + The created table. + + Raises + ------ + UnknownColumnNameError + If target_name matches none of the column names. + ValueError + If the target column is also a feature column. + ValueError + If no feature columns are specified. + + Examples + -------- + >>> from safeds.data.tabular.containers import Table, TaggedTable + >>> table = Table({"col1": ["a", "b", "c", "a"], "col2": [1, 2, 3, 4]}) + >>> tagged_table = TaggedTable._from_table(table, "col2", ["col1"]) + """ + table = table._as_table() + if target_name not in table.column_names: + raise UnknownColumnNameError([target_name]) + + # If no feature names are specified, use all columns except the target column + if feature_names is None: + feature_names = table.column_names + feature_names.remove(target_name) + + # Validate inputs + if target_name in feature_names: + raise ValueError(f"Column '{target_name}' cannot be both feature and target.") + if len(feature_names) == 0: + raise ValueError("At least one feature column must be specified.") + + # Create result + result = object.__new__(TaggedTable) + + result._data = table._data + result._schema = table.schema + result._features = table.keep_only_columns(feature_names) + result._target = table.get_column(target_name) + + return result + # ------------------------------------------------------------------------------------------------------------------ # Dunder methods # ------------------------------------------------------------------------------------------------------------------ def __init__( self, - data: Iterable, + data: Mapping[str, Sequence[Any]], target_name: str, feature_names: list[str] | None = None, - schema: Schema | None = None, ): - super().__init__(data, schema) + """ + Create a tagged table from a mapping of column names to their values. + + Parameters + ---------- + data : Mapping[str, Sequence[Any]] + The data. + target_name : str + Name of the target column. + feature_names : list[str] | None + Names of the feature columns. If None, all columns except the target column are used. + + Raises + ------ + ColumnLengthMismatchError + If columns have different lengths. + ValueError + If the target column is also a feature column. + ValueError + If no feature columns are specified. + + Examples + -------- + >>> from safeds.data.tabular.containers import TaggedTable + >>> table = TaggedTable({"a": [1, 2, 3], "b": [4, 5, 6]}, "b", ["a"]) + """ + super().__init__(data) + _data = Table(data) # If no feature names are specified, use all columns except the target column if feature_names is None: - feature_names = self.get_column_names() + feature_names = _data.column_names if target_name in feature_names: feature_names.remove(target_name) @@ -47,18 +158,8 @@ def __init__( if len(feature_names) == 0: raise ValueError("At least one feature column must be specified.") - self._features: Table = self.keep_only_columns(feature_names) - self._target: Column = self.get_column(target_name) - - def __repr__(self) -> str: - tmp = self._features.add_column(self._target) - header_info = "Target Column is '" + self._target.name + "'\n" - return header_info + tmp.__repr__() - - def __str__(self) -> str: - tmp = self._features.add_column(self._target) - header_info = "Target Column is '" + self._target.name + "'\n" - return header_info + tmp.__str__() + self._features: Table = _data.keep_only_columns(feature_names) + self._target: Column = _data.get_column(target_name) # ------------------------------------------------------------------------------------------------------------------ # Properties @@ -73,19 +174,643 @@ def target(self) -> Column: return self._target # ------------------------------------------------------------------------------------------------------------------ - # IPython integration + # Helpers + # ------------------------------------------------------------------------------------------------------------------ + + def _copy(self) -> TaggedTable: + """ + Return a copy of this tagged table. + + Returns + ------- + table : TaggedTable + The copy of this tagged table. + """ + return copy.deepcopy(self) + + # ------------------------------------------------------------------------------------------------------------------ + # Specific methods from TaggedTable class: + # ------------------------------------------------------------------------------------------------------------------ + + def add_column_as_feature(self, column: Column) -> TaggedTable: + """ + Return the original table with the provided column attached at the end, as a feature column. + + This table is not modified. + + Returns + ------- + result : TaggedTable + The table with the attached feature column. + + Raises + ------ + DuplicateColumnNameError + If the new column already exists. + ColumnSizeError + If the size of the column does not match the amount of rows. + """ + return TaggedTable._from_table( + super().add_column(column), + target_name=self.target.name, + feature_names=[*self.features.column_names, column.name], + ) + + def add_columns_as_features(self, columns: list[Column] | Table) -> TaggedTable: + """ + Return the original table with the provided columns attached at the end, as feature columns. + + This table is not modified. + + Returns + ------- + result : TaggedTable + The table with the attached feature columns. + + Raises + ------ + DuplicateColumnNameError + If the new column already exists. + ColumnSizeError + If the size of the column does not match the amount of rows. + """ + return TaggedTable._from_table( + super().add_columns(columns), + target_name=self.target.name, + feature_names=self.features.column_names + + [col.name for col in (columns.to_columns() if isinstance(columns, Table) else columns)], + ) + + # ------------------------------------------------------------------------------------------------------------------ + # Overriden methods from Table class: # ------------------------------------------------------------------------------------------------------------------ - def _ipython_display_(self) -> DisplayHandle: + def _as_table(self: TaggedTable) -> Table: + """ + Remove the tagging from a TaggedTable. + + The original TaggedTable is not modified. + + Parameters + ---------- + self: TaggedTable + The TaggedTable. + + Returns + ------- + table: Table + The table as an untagged Table, i.e. without the information about which columns are features or target. + + """ + return Table.from_columns(super().to_columns()) + + def add_column(self, column: Column) -> TaggedTable: + """ + Return the original table with the provided column attached at the end, as neither target nor feature column. + + This table is not modified. + + Returns + ------- + result : TaggedTable + The table with the column attached as neither target nor feature column. + + Raises + ------ + DuplicateColumnNameError + If the new column already exists. + ColumnSizeError + If the size of the column does not match the amount of rows. + """ + return TaggedTable._from_table( + super().add_column(column), + target_name=self.target.name, + feature_names=self.features.column_names, + ) + + def add_columns(self, columns: list[Column] | Table) -> TaggedTable: + """ + Add multiple columns to the table, as neither target nor feature columns. + + This table is not modified. + + Parameters + ---------- + columns : list[Column] or Table + The columns to be added. + + Returns + ------- + result: TaggedTable + A new table combining the original table and the given columns as neither target nor feature columns. + + Raises + ------ + ColumnSizeError + If at least one of the column sizes from the provided column list does not match the table. + DuplicateColumnNameError + If at least one column name from the provided column list already exists in the table. + """ + return TaggedTable._from_table( + super().add_columns(columns), + target_name=self.target.name, + feature_names=self.features.column_names, + ) + + def add_row(self, row: Row) -> TaggedTable: + """ + Add a row to the table. + + This table is not modified. + + Parameters + ---------- + row : Row + The row to be added. + + Returns + ------- + table : TaggedTable + A new table with the added row at the end. + + Raises + ------ + SchemaMismatchError + If the schema of the row does not match the table schema. + """ + return TaggedTable._from_table(super().add_row(row), target_name=self.target.name) + + def add_rows(self, rows: list[Row] | Table) -> TaggedTable: + """ + Add multiple rows to the table. + + This table is not modified. + + Parameters + ---------- + rows : list[Row] or Table + The rows to be added. + + Returns + ------- + result : TaggedTable + A new table which combines the original table and the given rows. + + Raises + ------ + SchemaMismatchError + If the schema of on of the row does not match the table schema. + """ + return TaggedTable._from_table(super().add_rows(rows), target_name=self.target.name) + + def filter_rows(self, query: Callable[[Row], bool]) -> TaggedTable: + """ + Return a table containing only rows that match the given Callable (e.g. lambda function). + + This table is not modified. + + Parameters + ---------- + query : lambda function + A Callable that is applied to all rows. + + Returns + ------- + table : TaggedTable + A table containing only the rows to match the query. + """ + return TaggedTable._from_table( + super().filter_rows(query), + target_name=self.target.name, + feature_names=self.features.column_names, + ) + + def keep_only_columns(self, column_names: list[str]) -> TaggedTable: """ - Return a display object for the column to be used in Jupyter Notebooks. + Return a table with only the given column(s). + + This table is not modified. + + Parameters + ---------- + column_names : list[str] + A list containing only the columns to be kept. + + Returns + ------- + table : TaggedTable + A table containing only the given column(s). + + Raises + ------ + UnknownColumnNameError + If any of the given columns does not exist. + IllegalSchemaModificationError + If none of the given columns is the target column or any of the feature columns. + """ + if self.target.name not in column_names: + raise IllegalSchemaModificationError("Must keep the target column.") + if len(set(self.features.column_names).intersection(set(column_names))) == 0: + raise IllegalSchemaModificationError("Must keep at least one feature column.") + return TaggedTable._from_table( + super().keep_only_columns(column_names), + target_name=self.target.name, + feature_names=sorted( + set(self.features.column_names).intersection(set(column_names)), + key={val: ix for ix, val in enumerate(self.features.column_names)}.__getitem__, + ), + ) + + def remove_columns(self, column_names: list[str]) -> TaggedTable: + """ + Remove the given column(s) from the table. + + This table is not modified. + + Parameters + ---------- + column_names : list[str] + The names of all columns to be dropped. + + Returns + ------- + table : TaggedTable + A table without the given columns. + + Raises + ------ + UnknownColumnNameError + If any of the given columns does not exist. + ColumnIsTargetError + If any of the given columns is the target column. + IllegalSchemaModificationError + If the given columns contain all the feature columns. + """ + if self.target.name in column_names: + raise ColumnIsTargetError(self.target.name) + if len(set(self.features.column_names) - set(column_names)) == 0: + raise IllegalSchemaModificationError("You cannot remove every feature column.") + return TaggedTable._from_table( + super().remove_columns(column_names), + target_name=self.target.name, + feature_names=sorted( + set(self.features.column_names) - set(column_names), + key={val: ix for ix, val in enumerate(self.features.column_names)}.__getitem__, + ), + ) + + def remove_columns_with_missing_values(self) -> TaggedTable: + """ + Remove every column that misses values. + + This table is not modified. + + Returns + ------- + table : TaggedTable + A table without the columns that contain missing values. + + Raises + ------ + ColumnIsTargetError + If any of the columns to be removed is the target column. + IllegalSchemaModificationError + If the columns to remove contain all the feature columns. + """ + table = super().remove_columns_with_missing_values() + if self.target.name not in table.column_names: + raise ColumnIsTargetError(self.target.name) + if len(set(self.features.column_names).intersection(set(table.column_names))) == 0: + raise IllegalSchemaModificationError("You cannot remove every feature column.") + return TaggedTable._from_table( + table, + self.target.name, + feature_names=sorted( + set(self.features.column_names).intersection(set(table.column_names)), + key={val: ix for ix, val in enumerate(self.features.column_names)}.__getitem__, + ), + ) + + def remove_columns_with_non_numerical_values(self) -> TaggedTable: + """ + Remove every column that contains non-numerical values. + + This table is not modified. + + Returns + ------- + table : TaggedTable + A table without the columns that contain non-numerical values. + + Raises + ------ + ColumnIsTargetError + If any of the columns to be removed is the target column. + IllegalSchemaModificationError + If the columns to remove contain all the feature columns. + """ + table = super().remove_columns_with_non_numerical_values() + if self.target.name not in table.column_names: + raise ColumnIsTargetError(self.target.name) + if len(set(self.features.column_names).intersection(set(table.column_names))) == 0: + raise IllegalSchemaModificationError("You cannot remove every feature column.") + return TaggedTable._from_table( + table, + self.target.name, + feature_names=sorted( + set(self.features.column_names).intersection(set(table.column_names)), + key={val: ix for ix, val in enumerate(self.features.column_names)}.__getitem__, + ), + ) + + def remove_duplicate_rows(self) -> TaggedTable: + """ + Remove all row duplicates. + + This table is not modified. + + Returns + ------- + result : TaggedTable + The table with the duplicate rows removed. + """ + return TaggedTable._from_table( + super().remove_duplicate_rows(), + target_name=self.target.name, + feature_names=self.features.column_names, + ) + + def remove_rows_with_missing_values(self) -> TaggedTable: + """ + Return a table without the rows that contain missing values. + + This table is not modified. + + Returns + ------- + table : TaggedTable + A table without the rows that contain missing values. + """ + return TaggedTable._from_table( + super().remove_rows_with_missing_values(), + target_name=self.target.name, + feature_names=self.features.column_names, + ) + + def remove_rows_with_outliers(self) -> TaggedTable: + """ + Remove all rows from the table that contain at least one outlier. + + We define an outlier as a value that has a distance of more than 3 standard deviations from the column mean. + Missing values are not considered outliers. They are also ignored during the calculation of the standard + deviation. + + This table is not modified. + + Returns + ------- + new_table : TaggedTable + A new table without rows containing outliers. + """ + return TaggedTable._from_table( + super().remove_rows_with_outliers(), + target_name=self.target.name, + feature_names=self.features.column_names, + ) + + def rename_column(self, old_name: str, new_name: str) -> TaggedTable: + """ + Rename a single column. + + This table is not modified. + + Parameters + ---------- + old_name : str + The old name of the target column + new_name : str + The new name of the target column Returns ------- - output : DisplayHandle - Output object. + table : TaggedTable + The Table with the renamed column. + + Raises + ------ + UnknownColumnNameError + If the specified old target column name does not exist. + DuplicateColumnNameError + If the specified new target column name already exists. + """ + return TaggedTable._from_table( + super().rename_column(old_name, new_name), + target_name=new_name if self.target.name == old_name else self.target.name, + feature_names=( + self.features.column_names + if old_name not in self.features.column_names + else [ + column_name if column_name != old_name else new_name for column_name in self.features.column_names + ] + ), + ) + + def replace_column(self, old_column_name: str, new_columns: list[Column]) -> TaggedTable: + """ + Replace the specified old column by a list of new columns. + + The order of columns is kept. + + If the column to be replaced is the target column, it must be replaced by exactly one column. That column becomes the new target column. + If the column to be replaced is a feature column, the new columns that replace it all become feature columns. + This table is not modified. + + Parameters + ---------- + old_column_name : str + The name of the column to be replaced. + new_columns : list[Column] + The new columns replacing the old column. + + Returns + ------- + result : TaggedTable + A table with the old column replaced by the new column. + + Raises + ------ + UnknownColumnNameError + If the old column does not exist. + DuplicateColumnNameError + If the new column already exists and the existing column is not affected by the replacement. + ColumnSizeError + If the size of the column does not match the amount of rows. + IllegalSchemaModificationError + If the target column would be removed or replaced by more than one column. + """ + if old_column_name == self.target.name: + if len(new_columns) != 1: + raise IllegalSchemaModificationError( + f'Target column "{self.target.name}" can only be replaced by exactly one new column.', + ) + else: + return TaggedTable._from_table( + super().replace_column(old_column_name, new_columns), + target_name=new_columns[0].name, + feature_names=self.features.column_names, + ) + else: + return TaggedTable._from_table( + super().replace_column(old_column_name, new_columns), + target_name=self.target.name, + feature_names=( + self.features.column_names + if old_column_name not in self.features.column_names + else self.features.column_names[: self.features.column_names.index(old_column_name)] + + [col.name for col in new_columns] + + self.features.column_names[self.features.column_names.index(old_column_name) + 1 :] + ), + ) + + def shuffle_rows(self) -> TaggedTable: + """ + Shuffle the table randomly. + + This table is not modified. + + Returns + ------- + result : TaggedTable + The shuffled Table. + """ + return TaggedTable._from_table( + super().shuffle_rows(), + target_name=self.target.name, + feature_names=self.features.column_names, + ) + + def slice_rows( + self, + start: int | None = None, + end: int | None = None, + step: int = 1, + ) -> TaggedTable: + """ + Slice a part of the table into a new table. + + This table is not modified. + + Parameters + ---------- + start : int + The first index of the range to be copied into a new table, None by default. + end : int + The last index of the range to be copied into a new table, None by default. + step : int + The step size used to iterate through the table, 1 by default. + + Returns + ------- + result : TaggedTable + The resulting table. + + Raises + ------ + IndexOutOfBoundsError + If the index is out of bounds. + """ + return TaggedTable._from_table( + super().slice_rows(start, end, step), + target_name=self.target.name, + feature_names=self.features.column_names, + ) + + def sort_columns( + self, + comparator: Callable[[Column, Column], int] = lambda col1, col2: (col1.name > col2.name) + - (col1.name < col2.name), + ) -> TaggedTable: + """ + Sort the columns of a `TaggedTable` with the given comparator and return a new `TaggedTable`. + + The comparator is a function that takes two columns `col1` and `col2` and + returns an integer: + + * If the function returns a negative number, `col1` will be ordered before `col2`. + * If the function returns a positive number, `col1` will be ordered after `col2`. + * If the function returns 0, the original order of `col1` and `col2` will be kept. + + If no comparator is given, the columns will be sorted alphabetically by their name. + + This table is not modified. + + Parameters + ---------- + comparator : Callable[[Column, Column], int] + The function used to compare two columns. + + Returns + ------- + new_table : TaggedTable + A new table with sorted columns. + """ + sorted_table = super().sort_columns(comparator) + return TaggedTable._from_table( + sorted_table, + target_name=self.target.name, + feature_names=sorted( + set(sorted_table.column_names).intersection(self.features.column_names), + key={val: ix for ix, val in enumerate(sorted_table.column_names)}.__getitem__, + ), + ) + + def sort_rows(self, comparator: Callable[[Row, Row], int]) -> TaggedTable: + """ + Sort the rows of a `TaggedTable` with the given comparator and return a new `TaggedTable`. + + The comparator is a function that takes two rows `row1` and `row2` and + returns an integer: + + * If the function returns a negative number, `row1` will be ordered before `row2`. + * If the function returns a positive number, `row1` will be ordered after `row2`. + * If the function returns 0, the original order of `row1` and `row2` will be kept. + + This table is not modified. + + Parameters + ---------- + comparator : Callable[[Row, Row], int] + The function used to compare two rows. + + Returns + ------- + new_table : TaggedTable + A new table with sorted rows. + """ + return TaggedTable._from_table( + super().sort_rows(comparator), + target_name=self.target.name, + feature_names=self.features.column_names, + ) + + def transform_column(self, name: str, transformer: Callable[[Row], Any]) -> TaggedTable: + """ + Transform provided column by calling provided transformer. + + This table is not modified. + + Returns + ------- + result : TaggedTable + The table with the transformed column. + + Raises + ------ + UnknownColumnNameError + If the column does not exist. """ - tmp = self._features.add_column(self._target) - header_info = "Target Column is '" + self._target.name + "'\n" - print(header_info) # noqa: T201 - return tmp._ipython_display_() + return TaggedTable._from_table( + super().transform_column(name, transformer), + target_name=self.target.name, + feature_names=self.features.column_names, + ) diff --git a/src/safeds/data/tabular/exceptions/__init__.py b/src/safeds/data/tabular/exceptions/__init__.py deleted file mode 100644 index 0627c2922..000000000 --- a/src/safeds/data/tabular/exceptions/__init__.py +++ /dev/null @@ -1,27 +0,0 @@ -"""Custom exceptions that can be raised when working with tabular data.""" - -from ._exceptions import ( - ColumnLengthMismatchError, - ColumnSizeError, - DuplicateColumnNameError, - IndexOutOfBoundsError, - MissingDataError, - MissingSchemaError, - NonNumericColumnError, - SchemaMismatchError, - TransformerNotFittedError, - UnknownColumnNameError, -) - -__all__ = [ - "ColumnLengthMismatchError", - "ColumnSizeError", - "DuplicateColumnNameError", - "IndexOutOfBoundsError", - "MissingDataError", - "MissingSchemaError", - "NonNumericColumnError", - "SchemaMismatchError", - "TransformerNotFittedError", - "UnknownColumnNameError", -] diff --git a/src/safeds/data/tabular/exceptions/_exceptions.py b/src/safeds/data/tabular/exceptions/_exceptions.py deleted file mode 100644 index 1dc68233a..000000000 --- a/src/safeds/data/tabular/exceptions/_exceptions.py +++ /dev/null @@ -1,101 +0,0 @@ -from __future__ import annotations - - -class UnknownColumnNameError(KeyError): - """ - Exception raised for trying to access an invalid column name. - - Parameters - ---------- - column_names : list[str] - The name of the column that was tried to be accessed. - """ - - def __init__(self, column_names: list[str]): - super().__init__(f"Could not find column(s) '{', '.join(column_names)}'") - - -class NonNumericColumnError(Exception): - """Exception raised for trying to do numerical operations on a non-numerical column.""" - - def __init__(self, column_info: str) -> None: - super().__init__(f"Tried to do a numerical operation on one or multiple non numerical Columns: \n{column_info}") - - -class DuplicateColumnNameError(Exception): - """ - Exception raised for trying to modify a table resulting in a duplicate column name. - - Parameters - ---------- - column_name : str - The name of the column that resulted in a duplicate. - """ - - def __init__(self, column_name: str): - super().__init__(f"Column '{column_name}' already exists.") - - -class IndexOutOfBoundsError(IndexError): - """ - Exception raised for trying to access an element by an index that does not exist in the underlying data. - - Parameters - ---------- - index : int - The wrongly used index. - """ - - def __init__(self, index: int): - super().__init__(f"There is no element at index '{index}'.") - - -class ColumnSizeError(Exception): - """ - Exception raised for trying to use a column of unsupported size. - - Parameters - ---------- - expected_size : str - The expected size of the column as an expression (e.g. 2, >0, !=0). - actual_size : str - The actual size of the column as an expression (e.g. 2, >0, !=0). - """ - - def __init__(self, expected_size: str, actual_size: str): - super().__init__(f"Expected a column of size {expected_size} but got column of size {actual_size}.") - - -class SchemaMismatchError(Exception): - """Exception raised when schemas are unequal.""" - - def __init__(self) -> None: - super().__init__("Failed because at least two schemas didn't match.") - - -class MissingSchemaError(Exception): - """Exception raised when a required schema is missing.""" - - def __init__(self) -> None: - super().__init__("Failed because a required schema is missing.") - - -class ColumnLengthMismatchError(Exception): - """Exception raised when the lengths of two or more columns do not match.""" - - def __init__(self, column_info: str): - super().__init__(f"The length of at least one column differs: \n{column_info}") - - -class MissingDataError(Exception): - """Exception raised if a function is not given enough data to succeed.""" - - def __init__(self, missing_data_info: str): - super().__init__(f"The function is missing data: \n{missing_data_info}") - - -class TransformerNotFittedError(Exception): - """Raised when a transformer is used before fitting it.""" - - def __init__(self) -> None: - super().__init__("The transformer has not been fitted yet.") diff --git a/src/safeds/data/tabular/transformation/__init__.py b/src/safeds/data/tabular/transformation/__init__.py index 29d4aba23..5f45314aa 100644 --- a/src/safeds/data/tabular/transformation/__init__.py +++ b/src/safeds/data/tabular/transformation/__init__.py @@ -1,14 +1,20 @@ """Classes for transforming tabular data.""" +from ._discretizer import Discretizer from ._imputer import Imputer from ._label_encoder import LabelEncoder from ._one_hot_encoder import OneHotEncoder +from ._range_scaler import RangeScaler +from ._standard_scaler import StandardScaler from ._table_transformer import InvertibleTableTransformer, TableTransformer __all__ = [ + "Discretizer", "Imputer", + "InvertibleTableTransformer", "LabelEncoder", "OneHotEncoder", - "InvertibleTableTransformer", + "RangeScaler", + "StandardScaler", "TableTransformer", ] diff --git a/src/safeds/data/tabular/transformation/_discretizer.py b/src/safeds/data/tabular/transformation/_discretizer.py new file mode 100644 index 000000000..38a6cf0f0 --- /dev/null +++ b/src/safeds/data/tabular/transformation/_discretizer.py @@ -0,0 +1,205 @@ +from __future__ import annotations + +from sklearn.preprocessing import KBinsDiscretizer as sk_KBinsDiscretizer + +from safeds.data.tabular.containers import Table +from safeds.data.tabular.transformation._table_transformer import TableTransformer +from safeds.exceptions import NonNumericColumnError, TransformerNotFittedError, UnknownColumnNameError + + +class Discretizer(TableTransformer): + """ + The Discretizer bins continuous data into intervals. + + Parameters + ---------- + number_of_bins: float + The number of bins to be created. + + Raises + ------ + ValueError + If the given number_of_bins is less than 2. + """ + + def __init__(self, number_of_bins: float = 5): + self._column_names: list[str] | None = None + self._wrapped_transformer: sk_KBinsDiscretizer | None = None + + if number_of_bins < 2: + raise ValueError("Parameter 'number_of_bins' must be >= 2.") + self._number_of_bins = number_of_bins + + def fit(self, table: Table, column_names: list[str] | None) -> Discretizer: + """ + Learn a transformation for a set of columns in a table. + + This transformer is not modified. + + Parameters + ---------- + table : Table + The table used to fit the transformer. + column_names : list[str] | None + The list of columns from the table used to fit the transformer. If `None`, all columns are used. + + Returns + ------- + fitted_transformer : TableTransformer + The fitted transformer. + + Raises + ------ + ValueError + If the table is empty. + NonNumericColumnError + If one of the columns, that should be fitted is non-numeric. + UnknownColumnNameError + If one of the columns, that should be fitted is not in the table. + """ + if table.number_of_rows == 0: + raise ValueError("The Discretizer cannot be fitted because the table contains 0 rows") + + if column_names is None: + column_names = table.column_names + else: + missing_columns = set(column_names) - set(table.column_names) + if len(missing_columns) > 0: + raise UnknownColumnNameError( + sorted( + missing_columns, + key={val: ix for ix, val in enumerate(column_names)}.__getitem__, + ), + ) + + for column in column_names: + if not table.get_column(column).type.is_numeric(): + raise NonNumericColumnError(f"{column} is of type {table.get_column(column).type}.") + + wrapped_transformer = sk_KBinsDiscretizer(n_bins=self._number_of_bins, encode="ordinal") + wrapped_transformer.fit(table._data[column_names]) + + result = Discretizer(self._number_of_bins) + result._wrapped_transformer = wrapped_transformer + result._column_names = column_names + + return result + + def transform(self, table: Table) -> Table: + """ + Apply the learned transformation to a table. + + The table is not modified. + + Parameters + ---------- + table : Table + The table to which the learned transformation is applied. + + Returns + ------- + transformed_table : Table + The transformed table. + + Raises + ------ + TransformerNotFittedError + If the transformer has not been fitted yet. + ValueError + If the table is empty. + UnknownColumnNameError + If one of the columns, that should be transformed is not in the table. + NonNumericColumnError + If one of the columns, that should be fitted is non-numeric. + """ + # Transformer has not been fitted yet + if self._wrapped_transformer is None or self._column_names is None: + raise TransformerNotFittedError + + if table.number_of_rows == 0: + raise ValueError("The table cannot be transformed because it contains 0 rows") + + # Input table does not contain all columns used to fit the transformer + missing_columns = set(self._column_names) - set(table.column_names) + if len(missing_columns) > 0: + raise UnknownColumnNameError( + sorted( + missing_columns, + key={val: ix for ix, val in enumerate(self._column_names)}.__getitem__, + ), + ) + + for column in self._column_names: + if not table.get_column(column).type.is_numeric(): + raise NonNumericColumnError(f"{column} is of type {table.get_column(column).type}.") + + data = table._data.copy() + data.columns = table.column_names + data[self._column_names] = self._wrapped_transformer.transform(data[self._column_names]) + return Table._from_pandas_dataframe(data) + + def is_fitted(self) -> bool: + """ + Check if the transformer is fitted. + + Returns + ------- + is_fitted : bool + Whether the transformer is fitted. + """ + return self._wrapped_transformer is not None + + def get_names_of_added_columns(self) -> list[str]: + """ + Get the names of all new columns that have been added by the Discretizer. + + Returns + ------- + added_columns : list[str] + A list of names of the added columns, ordered as they will appear in the table. + + Raises + ------ + TransformerNotFittedError + If the transformer has not been fitted yet. + """ + if not self.is_fitted(): + raise TransformerNotFittedError + return [] + + # (Must implement abstract method, cannot instantiate class otherwise.) + def get_names_of_changed_columns(self) -> list[str]: + """ + Get the names of all columns that may have been changed by the Discretizer. + + Returns + ------- + changed_columns : list[str] + The list of (potentially) changed column names, as passed to fit. + + Raises + ------ + TransformerNotFittedError + If the transformer has not been fitted yet. + """ + if self._column_names is None: + raise TransformerNotFittedError + return self._column_names + + def get_names_of_removed_columns(self) -> list[str]: + """ + Get the names of all columns that have been removed by the Discretizer. + + Returns + ------- + removed_columns : list[str] + A list of names of the removed columns, ordered as they appear in the table the Discretizer was fitted on. + + Raises + ------ + TransformerNotFittedError + If the transformer has not been fitted yet. + """ + if not self.is_fitted(): + raise TransformerNotFittedError + return [] diff --git a/src/safeds/data/tabular/transformation/_imputer.py b/src/safeds/data/tabular/transformation/_imputer.py index a6348033c..904c4b44c 100644 --- a/src/safeds/data/tabular/transformation/_imputer.py +++ b/src/safeds/data/tabular/transformation/_imputer.py @@ -1,14 +1,15 @@ from __future__ import annotations +import warnings from typing import Any import pandas as pd from sklearn.impute import SimpleImputer as sk_SimpleImputer from safeds.data.tabular.containers import Table -from safeds.data.tabular.exceptions import TransformerNotFittedError, UnknownColumnNameError from safeds.data.tabular.transformation._table_transformer import TableTransformer from safeds.data.tabular.typing import ImputerStrategy +from safeds.exceptions import NonNumericColumnError, TransformerNotFittedError, UnknownColumnNameError class Imputer(TableTransformer): @@ -75,7 +76,7 @@ def _augment_imputer(self, imputer: sk_SimpleImputer) -> None: imputer.strategy = "median" class Mode(ImputerStrategy): - """An imputation strategy for imputing missing data with mode values.""" + """An imputation strategy for imputing missing data with mode values. The lowest value will be used if there are multiple values with the same highest count.""" def __str__(self) -> str: return "Mode" @@ -90,10 +91,12 @@ def __init__(self, strategy: ImputerStrategy): self._column_names: list[str] | None = None # noinspection PyProtectedMember - def fit(self, table: Table, column_names: list[str] | None = None) -> Imputer: + def fit(self, table: Table, column_names: list[str] | None) -> Imputer: """ Learn a transformation for a set of columns in a table. + This transformer is not modified. + Parameters ---------- table : Table @@ -105,18 +108,59 @@ def fit(self, table: Table, column_names: list[str] | None = None) -> Imputer: ------- fitted_transformer : TableTransformer The fitted transformer. + + Raises + ------ + UnknownColumnNameError + If column_names contain a column name that is missing in the table + ValueError + If the table contains 0 rows + NonNumericColumnError + If the strategy is set to either Mean or Median and the specified columns of the table contain non-numerical data """ if column_names is None: - column_names = table.get_column_names() + column_names = table.column_names else: - missing_columns = set(column_names) - set(table.get_column_names()) + missing_columns = sorted(set(column_names) - set(table.column_names)) if len(missing_columns) > 0: - raise UnknownColumnNameError(list(missing_columns)) + raise UnknownColumnNameError(missing_columns) + + if table.number_of_rows == 0: + raise ValueError("The Imputer cannot be fitted because the table contains 0 rows") + + if (isinstance(self._strategy, Imputer.Strategy.Mean | Imputer.Strategy.Median)) and table.keep_only_columns( + column_names, + ).remove_columns_with_non_numerical_values().number_of_columns < len( + column_names, + ): + raise NonNumericColumnError( + str( + sorted( + set(table.keep_only_columns(column_names).column_names) + - set( + table.keep_only_columns(column_names) + .remove_columns_with_non_numerical_values() + .column_names, + ), + ), + ), + ) if isinstance(self._strategy, Imputer.Strategy.Mode): + multiple_most_frequent = {} for name in column_names: if len(table.get_column(name).mode()) > 1: - raise IndexError("There are multiple most frequent values in a column given for the Imputer") + multiple_most_frequent[name] = table.get_column(name).mode() + if len(multiple_most_frequent) > 0: + warnings.warn( + ( + "There are multiple most frequent values in a column given to the Imputer.\nThe lowest values" + " are being chosen in this cases. The following columns have multiple most frequent" + f" values:\n{multiple_most_frequent}" + ), + UserWarning, + stacklevel=2, + ) wrapped_transformer = sk_SimpleImputer() self._strategy._augment_imputer(wrapped_transformer) @@ -133,6 +177,8 @@ def transform(self, table: Table) -> Table: """ Apply the learned transformation to a table. + The table is not modified. + Parameters ---------- table : Table @@ -147,22 +193,40 @@ def transform(self, table: Table) -> Table: ------ TransformerNotFittedError If the transformer has not been fitted yet. + UnknownColumnNameError + If the input table does not contain all columns used to fit the transformer + ValueError + If the table contains 0 rows """ # Transformer has not been fitted yet if self._wrapped_transformer is None or self._column_names is None: raise TransformerNotFittedError # Input table does not contain all columns used to fit the transformer - missing_columns = set(self._column_names) - set(table.get_column_names()) + missing_columns = sorted(set(self._column_names) - set(table.column_names)) if len(missing_columns) > 0: - raise UnknownColumnNameError(list(missing_columns)) + raise UnknownColumnNameError(missing_columns) + + if table.number_of_rows == 0: + raise ValueError("The Imputer cannot transform the table because it contains 0 rows") + + if table.keep_only_columns(self._column_names).remove_columns_with_missing_values().number_of_columns > 0: + warnings.warn( + ( + "The columns" + f" {table.keep_only_columns(self._column_names).remove_columns_with_missing_values().column_names} have" + " no missing values, so the Imputer did not change these columns" + ), + UserWarning, + stacklevel=2, + ) data = table._data.copy() data[self._column_names] = pd.DataFrame( self._wrapped_transformer.transform(data[self._column_names]), columns=self._column_names, ) - return Table(data, table.schema) + return Table._from_pandas_dataframe(data, table.schema) def is_fitted(self) -> bool: """ @@ -174,3 +238,58 @@ def is_fitted(self) -> bool: Whether the transformer is fitted. """ return self._wrapped_transformer is not None + + def get_names_of_added_columns(self) -> list[str]: + """ + Get the names of all new columns that have been added by the Imputer. + + Returns + ------- + added_columns : list[str] + A list of names of the added columns, ordered as they will appear in the table. + + Raises + ------ + TransformerNotFittedError + If the transformer has not been fitted yet. + """ + if not self.is_fitted(): + raise TransformerNotFittedError + return [] + + # (Must implement abstract method, cannot instantiate class otherwise.) + def get_names_of_changed_columns(self) -> list[str]: + """ + Get the names of all columns that may have been changed by the Imputer. + + Returns + ------- + changed_columns : list[str] + The list of (potentially) changed column names, as passed to fit. + + Raises + ------ + TransformerNotFittedError + If the transformer has not been fitted yet. + """ + if self._column_names is None: + raise TransformerNotFittedError + return self._column_names + + def get_names_of_removed_columns(self) -> list[str]: + """ + Get the names of all columns that have been removed by the Imputer. + + Returns + ------- + removed_columns : list[str] + A list of names of the removed columns, ordered as they appear in the table the Imputer was fitted on. + + Raises + ------ + TransformerNotFittedError + If the transformer has not been fitted yet. + """ + if not self.is_fitted(): + raise TransformerNotFittedError + return [] diff --git a/src/safeds/data/tabular/transformation/_label_encoder.py b/src/safeds/data/tabular/transformation/_label_encoder.py index a8d2d999e..20fec0436 100644 --- a/src/safeds/data/tabular/transformation/_label_encoder.py +++ b/src/safeds/data/tabular/transformation/_label_encoder.py @@ -1,22 +1,14 @@ from __future__ import annotations import warnings -from typing import Any from sklearn.preprocessing import OrdinalEncoder as sk_OrdinalEncoder from safeds.data.tabular.containers import Table -from safeds.data.tabular.exceptions import TransformerNotFittedError, UnknownColumnNameError from safeds.data.tabular.transformation._table_transformer import ( InvertibleTableTransformer, ) - - -def warn(*_: Any, **__: Any) -> None: - pass - - -warnings.warn = warn +from safeds.exceptions import NonNumericColumnError, TransformerNotFittedError, UnknownColumnNameError # noinspection PyProtectedMember @@ -27,10 +19,12 @@ def __init__(self) -> None: self._wrapped_transformer: sk_OrdinalEncoder | None = None self._column_names: list[str] | None = None - def fit(self, table: Table, column_names: list[str] | None = None) -> LabelEncoder: + def fit(self, table: Table, column_names: list[str] | None) -> LabelEncoder: """ Learn a transformation for a set of columns in a table. + This transformer is not modified. + Parameters ---------- table : Table @@ -42,13 +36,35 @@ def fit(self, table: Table, column_names: list[str] | None = None) -> LabelEncod ------- fitted_transformer : TableTransformer The fitted transformer. + + Raises + ------ + UnknownColumnNameError + If column_names contain a column name that is missing in the table + ValueError + If the table contains 0 rows """ if column_names is None: - column_names = table.get_column_names() + column_names = table.column_names else: - missing_columns = set(column_names) - set(table.get_column_names()) + missing_columns = sorted(set(column_names) - set(table.column_names)) if len(missing_columns) > 0: - raise UnknownColumnNameError(list(missing_columns)) + raise UnknownColumnNameError(missing_columns) + + if table.number_of_rows == 0: + raise ValueError("The LabelEncoder cannot transform the table because it contains 0 rows") + + if table.keep_only_columns(column_names).remove_columns_with_non_numerical_values().number_of_columns > 0: + warnings.warn( + ( + "The columns" + f" {table.keep_only_columns(column_names).remove_columns_with_non_numerical_values().column_names} contain" + " numerical data. The LabelEncoder is designed to encode non-numerical values into numerical" + " values" + ), + UserWarning, + stacklevel=2, + ) wrapped_transformer = sk_OrdinalEncoder() wrapped_transformer.fit(table._data[column_names]) @@ -63,6 +79,8 @@ def transform(self, table: Table) -> Table: """ Apply the learned transformation to a table. + The table is not modified. + Parameters ---------- table : Table @@ -77,25 +95,34 @@ def transform(self, table: Table) -> Table: ------ TransformerNotFittedError If the transformer has not been fitted yet. + UnknownColumnNameError + If the input table does not contain all columns used to fit the transformer + ValueError + If the table contains 0 rows """ # Transformer has not been fitted yet if self._wrapped_transformer is None or self._column_names is None: raise TransformerNotFittedError # Input table does not contain all columns used to fit the transformer - missing_columns = set(self._column_names) - set(table.get_column_names()) + missing_columns = sorted(set(self._column_names) - set(table.column_names)) if len(missing_columns) > 0: - raise UnknownColumnNameError(list(missing_columns)) + raise UnknownColumnNameError(missing_columns) + + if table.number_of_rows == 0: + raise ValueError("The LabelEncoder cannot transform the table because it contains 0 rows") data = table._data.copy() - data.columns = table.get_column_names() + data.columns = table.column_names data[self._column_names] = self._wrapped_transformer.transform(data[self._column_names]) - return Table(data) + return Table._from_pandas_dataframe(data) def inverse_transform(self, transformed_table: Table) -> Table: """ Undo the learned transformation. + The table is not modified. + Parameters ---------- transformed_table : Table @@ -110,15 +137,44 @@ def inverse_transform(self, transformed_table: Table) -> Table: ------ TransformerNotFittedError If the transformer has not been fitted yet. + UnknownColumnNameError + If the input table does not contain all columns used to fit the transformer + NonNumericColumnError + If the specified columns of the input table contain non-numerical data + ValueError + If the table contains 0 rows """ # Transformer has not been fitted yet if self._wrapped_transformer is None or self._column_names is None: raise TransformerNotFittedError + missing_columns = sorted(set(self._column_names) - set(transformed_table.column_names)) + if len(missing_columns) > 0: + raise UnknownColumnNameError(missing_columns) + + if transformed_table.keep_only_columns( + self._column_names, + ).remove_columns_with_non_numerical_values().number_of_columns < len(self._column_names): + raise NonNumericColumnError( + str( + sorted( + set(self._column_names) + - set( + transformed_table.keep_only_columns(self._column_names) + .remove_columns_with_non_numerical_values() + .column_names, + ), + ), + ), + ) + + if transformed_table.number_of_rows == 0: + raise ValueError("The LabelEncoder cannot inverse transform the table because it contains 0 rows") + data = transformed_table._data.copy() - data.columns = transformed_table.get_column_names() + data.columns = transformed_table.column_names data[self._column_names] = self._wrapped_transformer.inverse_transform(data[self._column_names]) - return Table(data) + return Table._from_pandas_dataframe(data) def is_fitted(self) -> bool: """ @@ -130,3 +186,58 @@ def is_fitted(self) -> bool: Whether the transformer is fitted. """ return self._wrapped_transformer is not None + + def get_names_of_added_columns(self) -> list[str]: + """ + Get the names of all new columns that have been added by the LabelEncoder. + + Returns + ------- + added_columns : list[str] + A list of names of the added columns, ordered as they will appear in the table. + + Raises + ------ + TransformerNotFittedError + If the transformer has not been fitted yet. + """ + if not self.is_fitted(): + raise TransformerNotFittedError + return [] + + # (Must implement abstract method, cannot instantiate class otherwise.) + def get_names_of_changed_columns(self) -> list[str]: + """ + Get the names of all columns that may have been changed by the LabelEncoder. + + Returns + ------- + changed_columns : list[str] + The list of (potentially) changed column names, as passed to fit. + + Raises + ------ + TransformerNotFittedError + If the transformer has not been fitted yet. + """ + if self._column_names is None: + raise TransformerNotFittedError + return self._column_names + + def get_names_of_removed_columns(self) -> list[str]: + """ + Get the names of all columns that have been removed by the LabelEncoder. + + Returns + ------- + removed_columns : list[str] + A list of names of the removed columns, ordered as they appear in the table the LabelEncoder was fitted on. + + Raises + ------ + TransformerNotFittedError + If the transformer has not been fitted yet. + """ + if not self.is_fitted(): + raise TransformerNotFittedError + return [] diff --git a/src/safeds/data/tabular/transformation/_one_hot_encoder.py b/src/safeds/data/tabular/transformation/_one_hot_encoder.py index 3c84ce5c0..9a198ea0e 100644 --- a/src/safeds/data/tabular/transformation/_one_hot_encoder.py +++ b/src/safeds/data/tabular/transformation/_one_hot_encoder.py @@ -1,27 +1,79 @@ from __future__ import annotations -import pandas as pd -from sklearn.preprocessing import OneHotEncoder as sk_OneHotEncoder +import warnings +from collections import Counter +from typing import Any -from safeds.data.tabular.containers import Table -from safeds.data.tabular.exceptions import TransformerNotFittedError, UnknownColumnNameError +import numpy as np + +from safeds.data.tabular.containers import Column, Table from safeds.data.tabular.transformation._table_transformer import ( InvertibleTableTransformer, ) +from safeds.exceptions import ( + NonNumericColumnError, + TransformerNotFittedError, + UnknownColumnNameError, + ValueNotPresentWhenFittedError, +) class OneHotEncoder(InvertibleTableTransformer): - """Encodes categorical columns to numerical features [0,1] that represent the existence for each value.""" + """ + A way to deal with categorical features that is particularly useful for unordered (i.e. nominal) data. + + It replaces a column with a set of columns, each representing a unique value in the original column. The value of + each new column is 1 if the original column had that value, and 0 otherwise. Take the following table as an + example: + + | col1 | + |------| + | "a" | + | "b" | + | "c" | + | "a" | + + The one-hot encoding of this table is: + + | col1__a | col1__b | col1__c | + |---------|---------|---------| + | 1 | 0 | 0 | + | 0 | 1 | 0 | + | 0 | 0 | 1 | + | 1 | 0 | 0 | + + The name "one-hot" comes from the fact that each row has exactly one 1 in it, and the rest of the values are 0s. + One-hot encoding is closely related to dummy variable / indicator variables, which are used in statistics. + + Examples + -------- + >>> from safeds.data.tabular.containers import Table + >>> from safeds.data.tabular.transformation import OneHotEncoder + >>> table = Table({"col1": ["a", "b", "c", "a"]}) + >>> transformer = OneHotEncoder() + >>> transformer.fit_and_transform(table, ["col1"]) + col1__a col1__b col1__c + 0 1.0 0.0 0.0 + 1 0.0 1.0 0.0 + 2 0.0 0.0 1.0 + 3 1.0 0.0 0.0 + """ def __init__(self) -> None: - self._wrapped_transformer: sk_OneHotEncoder | None = None + # Maps each old column to (list of) new columns created from it: self._column_names: dict[str, list[str]] | None = None + # Maps concrete values (tuples of old column and value) to corresponding new column names: + self._value_to_column: dict[tuple[str, Any], str] | None = None + # Maps nan values (str of old column) to corresponding new column name + self._value_to_column_nans: dict[str, str] | None = None # noinspection PyProtectedMember - def fit(self, table: Table, column_names: list[str] | None = None) -> OneHotEncoder: + def fit(self, table: Table, column_names: list[str] | None) -> OneHotEncoder: """ Learn a transformation for a set of columns in a table. + This transformer is not modified. + Parameters ---------- table : Table @@ -33,26 +85,71 @@ def fit(self, table: Table, column_names: list[str] | None = None) -> OneHotEnco ------- fitted_transformer : TableTransformer The fitted transformer. + + Raises + ------ + UnknownColumnNameError + If column_names contain a column name that is missing in the table + ValueError + If the table contains 0 rows """ if column_names is None: - column_names = table.get_column_names() + column_names = table.column_names else: - missing_columns = set(column_names) - set(table.get_column_names()) + missing_columns = sorted(set(column_names) - set(table.column_names)) if len(missing_columns) > 0: - raise UnknownColumnNameError(list(missing_columns)) + raise UnknownColumnNameError(missing_columns) + + if table.number_of_rows == 0: + raise ValueError("The OneHotEncoder cannot be fitted because the table contains 0 rows") + + if ( + table._as_table() + .keep_only_columns(column_names) + .remove_columns_with_non_numerical_values() + .number_of_columns + > 0 + ): + warnings.warn( + ( + "The columns" + f" {table._as_table().keep_only_columns(column_names).remove_columns_with_non_numerical_values().column_names} contain" + " numerical data. The OneHotEncoder is designed to encode non-numerical values into numerical" + " values" + ), + UserWarning, + stacklevel=2, + ) data = table._data.copy() - data.columns = table.get_column_names() - - wrapped_transformer = sk_OneHotEncoder() - wrapped_transformer.fit(data[column_names]) + data.columns = table.column_names result = OneHotEncoder() - result._wrapped_transformer = wrapped_transformer - result._column_names = { - column: [f"{column}_{element}" for element in table.get_column(column).get_unique_values()] - for column in column_names - } + + result._column_names = {} + result._value_to_column = {} + result._value_to_column_nans = {} + + # Keep track of number of occurrences of column names; + # initially all old column names appear exactly once: + name_counter = Counter(data.columns) + + # Iterate through all columns to-be-changed: + for column in column_names: + result._column_names[column] = [] + for element in table.get_column(column).get_unique_values(): + base_name = f"{column}__{element}" + name_counter[base_name] += 1 + new_column_name = base_name + # Check if newly created name matches some other existing column name: + if name_counter[base_name] > 1: + new_column_name += f"#{name_counter[base_name]}" + # Update dictionary entries: + result._column_names[column] += [new_column_name] + if isinstance(element, float) and np.isnan(element): + result._value_to_column_nans[column] = new_column_name + else: + result._value_to_column[(column, element)] = new_column_name return result @@ -61,6 +158,8 @@ def transform(self, table: Table) -> Table: """ Apply the learned transformation to a table. + The table is not modified. + Parameters ---------- table : Table @@ -75,45 +174,78 @@ def transform(self, table: Table) -> Table: ------ TransformerNotFittedError If the transformer has not been fitted yet. + UnknownColumnNameError + If the input table does not contain all columns used to fit the transformer + ValueError + If the table contains 0 rows + ValueNotPresentWhenFittedError + If a column in the to-be-transformed table contains a new value that was not already present in the table the OneHotEncoder was fitted on """ # Transformer has not been fitted yet - if self._wrapped_transformer is None or self._column_names is None: + if self._column_names is None or self._value_to_column is None or self._value_to_column_nans is None: raise TransformerNotFittedError # Input table does not contain all columns used to fit the transformer - missing_columns = set(self._column_names.keys()) - set(table.get_column_names()) + missing_columns = sorted(set(self._column_names.keys()) - set(table.column_names)) if len(missing_columns) > 0: - raise UnknownColumnNameError(list(missing_columns)) - - original = table._data.copy() - original.columns = table.schema.get_column_names() - - one_hot_encoded = pd.DataFrame( - self._wrapped_transformer.transform(original[self._column_names.keys()]).toarray(), - ) - one_hot_encoded.columns = self._wrapped_transformer.get_feature_names_out() - - unchanged = original.drop(self._column_names.keys(), axis=1) - - res = Table(pd.concat([unchanged, one_hot_encoded], axis=1)) + raise UnknownColumnNameError(missing_columns) + + if table.number_of_rows == 0: + raise ValueError("The LabelEncoder cannot transform the table because it contains 0 rows") + + encoded_values = {} + for new_column_name in self._value_to_column.values(): + encoded_values[new_column_name] = [0.0 for _ in range(table.number_of_rows)] + for new_column_name in self._value_to_column_nans.values(): + encoded_values[new_column_name] = [0.0 for _ in range(table.number_of_rows)] + + values_not_present_when_fitted = [] + for old_column_name in self._column_names: + for i in range(table.number_of_rows): + value = table.get_column(old_column_name).get_value(i) + try: + if isinstance(value, float) and np.isnan(value): + new_column_name = self._value_to_column_nans[old_column_name] + else: + new_column_name = self._value_to_column[(old_column_name, value)] + encoded_values[new_column_name][i] = 1.0 + except KeyError: + # This happens when a column in the to-be-transformed table contains a new value that was not + # already present in the table the OneHotEncoder was fitted on. + values_not_present_when_fitted.append((value, old_column_name)) + + for new_column in self._column_names[old_column_name]: + table = table.add_column(Column(new_column, encoded_values[new_column])) + + if len(values_not_present_when_fitted) > 0: + raise ValueNotPresentWhenFittedError(values_not_present_when_fitted) + + # New columns may not be sorted: column_names = [] - - for name in table.get_column_names(): + for name in table.column_names: if name not in self._column_names.keys(): column_names.append(name) else: column_names.extend( - [f_name for f_name in self._wrapped_transformer.get_feature_names_out() if f_name.startswith(name)], + [f_name for f_name in self._value_to_column.values() if f_name.startswith(name)] + + [f_name for f_name in self._value_to_column_nans.values() if f_name.startswith(name)], ) - res = res.sort_columns(lambda col1, col2: column_names.index(col1.name) - column_names.index(col2.name)) - return res + # Drop old, non-encoded columns: + # (Don't do this earlier - we need the old column nams for sorting, + # plus we need to prevent the table from possibly having 0 columns temporarily.) + table = table.remove_columns(list(self._column_names.keys())) + + # Apply sorting and return: + return table.sort_columns(lambda col1, col2: column_names.index(col1.name) - column_names.index(col2.name)) # noinspection PyProtectedMember def inverse_transform(self, transformed_table: Table) -> Table: """ Undo the learned transformation. + The table is not modified. + Parameters ---------- transformed_table : Table @@ -128,38 +260,77 @@ def inverse_transform(self, transformed_table: Table) -> Table: ------ TransformerNotFittedError If the transformer has not been fitted yet. + UnknownColumnNameError + If the input table does not contain all columns used to fit the transformer + NonNumericColumnError + If the transformed columns of the input table contain non-numerical data + ValueError + If the table contains 0 rows """ # Transformer has not been fitted yet - if self._wrapped_transformer is None or self._column_names is None: + if self._column_names is None or self._value_to_column is None or self._value_to_column_nans is None: raise TransformerNotFittedError - data = transformed_table._data.copy() - data.columns = transformed_table.get_column_names() + _transformed_column_names = [item for sublist in self._column_names.values() for item in sublist] - decoded = pd.DataFrame( - self._wrapped_transformer.inverse_transform( - transformed_table.keep_only_columns(self._wrapped_transformer.get_feature_names_out())._data, - ), - columns=list(self._column_names.keys()), - ) - unchanged = data.drop(self._wrapped_transformer.get_feature_names_out(), axis=1) + missing_columns = sorted(set(_transformed_column_names) - set(transformed_table.column_names)) + if len(missing_columns) > 0: + raise UnknownColumnNameError(missing_columns) + + if transformed_table._as_table().keep_only_columns( + _transformed_column_names, + ).remove_columns_with_non_numerical_values().number_of_columns < len(_transformed_column_names): + raise NonNumericColumnError( + str( + sorted( + set(_transformed_column_names) + - set( + transformed_table.keep_only_columns(_transformed_column_names) + .remove_columns_with_non_numerical_values() + .column_names, + ), + ), + ), + ) + + if transformed_table.number_of_rows == 0: + raise ValueError("The OneHotEncoder cannot inverse transform the table because it contains 0 rows") + + original_columns = {} + for original_column_name in self._column_names: + original_columns[original_column_name] = [None for _ in range(transformed_table.number_of_rows)] + + for original_column_name, value in self._value_to_column: + constructed_column = self._value_to_column[(original_column_name, value)] + for i in range(transformed_table.number_of_rows): + if transformed_table.get_column(constructed_column)[i] == 1.0: + original_columns[original_column_name][i] = value + + table = transformed_table + + for column_name, encoded_column in original_columns.items(): + table = table.add_column(Column(column_name, encoded_column)) - res = Table(pd.concat([unchanged, decoded], axis=1)) column_names = [ - name - if name not in [value for value_list in list(self._column_names.values()) for value in value_list] - else list(self._column_names.keys())[ - [ - list(self._column_names.values()).index(value) - for value in list(self._column_names.values()) - if name in value - ][0] - ] - for name in transformed_table.get_column_names() + ( + name + if name not in [value for value_list in list(self._column_names.values()) for value in value_list] + else list(self._column_names.keys())[ + [ + list(self._column_names.values()).index(value) + for value in list(self._column_names.values()) + if name in value + ][0] + ] + ) + for name in table.column_names ] - res = res.sort_columns(lambda col1, col2: column_names.index(col1.name) - column_names.index(col2.name)) - return res + # Drop old column names: + table = table.remove_columns(list(self._value_to_column.values())) + table = table.remove_columns(list(self._value_to_column_nans.values())) + + return table.sort_columns(lambda col1, col2: column_names.index(col1.name) - column_names.index(col2.name)) def is_fitted(self) -> bool: """ @@ -170,4 +341,63 @@ def is_fitted(self) -> bool: is_fitted : bool Whether the transformer is fitted. """ - return self._wrapped_transformer is not None + return ( + self._column_names is not None + and self._value_to_column is not None + and self._value_to_column_nans is not None + ) + + def get_names_of_added_columns(self) -> list[str]: + """ + Get the names of all new columns that have been added by the OneHotEncoder. + + Returns + ------- + added_columns : list[str] + A list of names of the added columns, ordered as they will appear in the table. + + Raises + ------ + TransformerNotFittedError + If the transformer has not been fitted yet. + """ + if self._column_names is None: + raise TransformerNotFittedError + return [name for column_names in self._column_names.values() for name in column_names] + + # (Must implement abstract method, cannot instantiate class otherwise.) + def get_names_of_changed_columns(self) -> list[str]: + """ + Get the names of all columns that have been changed by the OneHotEncoder (none). + + Returns + ------- + changed_columns : list[str] + The empty list. + + Raises + ------ + TransformerNotFittedError + If the transformer has not been fitted yet. + """ + if not self.is_fitted(): + raise TransformerNotFittedError + return [] + + def get_names_of_removed_columns(self) -> list[str]: + """ + Get the names of all columns that have been removed by the OneHotEncoder. + + Returns + ------- + removed_columns : list[str] + A list of names of the removed columns, ordered as they appear in the table the OneHotEncoder was fitted on. + + Raises + ------ + TransformerNotFittedError + If the transformer has not been fitted yet. + """ + if self._column_names is None: + raise TransformerNotFittedError + return list(self._column_names.keys()) diff --git a/src/safeds/data/tabular/transformation/_range_scaler.py b/src/safeds/data/tabular/transformation/_range_scaler.py new file mode 100644 index 000000000..e7f8c16ba --- /dev/null +++ b/src/safeds/data/tabular/transformation/_range_scaler.py @@ -0,0 +1,284 @@ +from __future__ import annotations + +from sklearn.preprocessing import MinMaxScaler as sk_MinMaxScaler + +from safeds.data.tabular.containers import Table +from safeds.data.tabular.transformation._table_transformer import InvertibleTableTransformer +from safeds.exceptions import NonNumericColumnError, TransformerNotFittedError, UnknownColumnNameError + + +class RangeScaler(InvertibleTableTransformer): + """ + The RangeScaler transforms column values by scaling each value to a given range. + + Parameters + ---------- + minimum : float + The minimum of the new range after the transformation + maximum : float + The maximum of the new range after the transformation + Raises + ------ + ValueError + If the given minimum is greater or equal to the given maximum + """ + + def __init__(self, minimum: float = 0.0, maximum: float = 1.0): + self._column_names: list[str] | None = None + self._wrapped_transformer: sk_MinMaxScaler | None = None + if minimum >= maximum: + raise ValueError('Parameter "maximum" must be higher than parameter "minimum".') + self._minimum = minimum + self._maximum = maximum + + def fit(self, table: Table, column_names: list[str] | None) -> RangeScaler: + """ + Learn a transformation for a set of columns in a table. + + This transformer is not modified. + + Parameters + ---------- + table : Table + The table used to fit the transformer. + column_names : Optional[list[str]] + The list of columns from the table used to fit the transformer. If `None`, all columns are used. + + Returns + ------- + fitted_transformer : TableTransformer + The fitted transformer. + + Raises + ------ + UnknownColumnNameError + If column_names contain a column name that is missing in the table + NonNumericColumnError + If at least one of the specified columns in the table contains non-numerical data + ValueError + If the table contains 0 rows + """ + if column_names is None: + column_names = table.column_names + else: + missing_columns = sorted(set(column_names) - set(table.column_names)) + if len(missing_columns) > 0: + raise UnknownColumnNameError(missing_columns) + + if ( + table.keep_only_columns(column_names).remove_columns_with_non_numerical_values().number_of_columns + < table.keep_only_columns(column_names).number_of_columns + ): + raise NonNumericColumnError( + str( + sorted( + set(table.keep_only_columns(column_names).column_names) + - set( + table.keep_only_columns(column_names) + .remove_columns_with_non_numerical_values() + .column_names, + ), + ), + ), + ) + + if table.number_of_rows == 0: + raise ValueError("The RangeScaler cannot be fitted because the table contains 0 rows") + + wrapped_transformer = sk_MinMaxScaler((self._minimum, self._maximum)) + wrapped_transformer.fit(table._data[column_names]) + + result = RangeScaler() + result._wrapped_transformer = wrapped_transformer + result._column_names = column_names + + return result + + def transform(self, table: Table) -> Table: + """ + Apply the learned transformation to a table. + + The table is not modified. + + Parameters + ---------- + table : Table + The table to which the learned transformation is applied. + + Returns + ------- + transformed_table : Table + The transformed table. + + Raises + ------ + TransformerNotFittedError + If the transformer has not been fitted yet. + UnknownColumnNameError + If the input table does not contain all columns used to fit the transformer + NonNumericColumnError + If at least one of the columns in the input table that is used to fit contains non-numerical data + ValueError + If the table contains 0 rows + """ + # Transformer has not been fitted yet + if self._wrapped_transformer is None or self._column_names is None: + raise TransformerNotFittedError + + # Input table does not contain all columns used to fit the transformer + missing_columns = sorted(set(self._column_names) - set(table.column_names)) + if len(missing_columns) > 0: + raise UnknownColumnNameError(missing_columns) + + if ( + table.keep_only_columns(self._column_names).remove_columns_with_non_numerical_values().number_of_columns + < table.keep_only_columns(self._column_names).number_of_columns + ): + raise NonNumericColumnError( + str( + sorted( + set(table.keep_only_columns(self._column_names).column_names) + - set( + table.keep_only_columns(self._column_names) + .remove_columns_with_non_numerical_values() + .column_names, + ), + ), + ), + ) + + if table.number_of_rows == 0: + raise ValueError("The RangeScaler cannot transform the table because it contains 0 rows") + + data = table._data.copy() + data.columns = table.column_names + data[self._column_names] = self._wrapped_transformer.transform(data[self._column_names]) + return Table._from_pandas_dataframe(data) + + def inverse_transform(self, transformed_table: Table) -> Table: + """ + Undo the learned transformation. + + The table is not modified. + + Parameters + ---------- + transformed_table : Table + The table to be transformed back to the original version. + + Returns + ------- + table : Table + The original table. + + Raises + ------ + TransformerNotFittedError + If the transformer has not been fitted yet. + UnknownColumnNameError + If the input table does not contain all columns used to fit the transformer + NonNumericColumnError + If the transformed columns of the input table contain non-numerical data + ValueError + If the table contains 0 rows + """ + # Transformer has not been fitted yet + if self._wrapped_transformer is None or self._column_names is None: + raise TransformerNotFittedError + + missing_columns = sorted(set(self._column_names) - set(transformed_table.column_names)) + if len(missing_columns) > 0: + raise UnknownColumnNameError(missing_columns) + + if ( + transformed_table.keep_only_columns(self._column_names) + .remove_columns_with_non_numerical_values() + .number_of_columns + < transformed_table.keep_only_columns(self._column_names).number_of_columns + ): + raise NonNumericColumnError( + str( + sorted( + set(transformed_table.keep_only_columns(self._column_names).column_names) + - set( + transformed_table.keep_only_columns(self._column_names) + .remove_columns_with_non_numerical_values() + .column_names, + ), + ), + ), + ) + + if transformed_table.number_of_rows == 0: + raise ValueError("The RangeScaler cannot transform the table because it contains 0 rows") + + data = transformed_table._data.copy() + data.columns = transformed_table.column_names + data[self._column_names] = self._wrapped_transformer.inverse_transform(data[self._column_names]) + return Table._from_pandas_dataframe(data) + + def is_fitted(self) -> bool: + """ + Check if the transformer is fitted. + + Returns + ------- + is_fitted : bool + Whether the transformer is fitted. + """ + return self._wrapped_transformer is not None + + def get_names_of_added_columns(self) -> list[str]: + """ + Get the names of all new columns that have been added by the RangeScaler. + + Returns + ------- + added_columns : list[str] + A list of names of the added columns, ordered as they will appear in the table. + + Raises + ------ + TransformerNotFittedError + If the transformer has not been fitted yet. + """ + if not self.is_fitted(): + raise TransformerNotFittedError + return [] + + # (Must implement abstract method, cannot instantiate class otherwise.) + def get_names_of_changed_columns(self) -> list[str]: + """ + Get the names of all columns that may have been changed by the RangeScaler. + + Returns + ------- + changed_columns : list[str] + The list of (potentially) changed column names, as passed to fit. + + Raises + ------ + TransformerNotFittedError + If the transformer has not been fitted yet. + """ + if self._column_names is None: + raise TransformerNotFittedError + return self._column_names + + def get_names_of_removed_columns(self) -> list[str]: + """ + Get the names of all columns that have been removed by the RangeScaler. + + Returns + ------- + removed_columns : list[str] + A list of names of the removed columns, ordered as they appear in the table the RangeScaler was fitted on. + + Raises + ------ + TransformerNotFittedError + If the transformer has not been fitted yet. + """ + if not self.is_fitted(): + raise TransformerNotFittedError + return [] diff --git a/src/safeds/data/tabular/transformation/_standard_scaler.py b/src/safeds/data/tabular/transformation/_standard_scaler.py new file mode 100644 index 000000000..3c190c58f --- /dev/null +++ b/src/safeds/data/tabular/transformation/_standard_scaler.py @@ -0,0 +1,267 @@ +from __future__ import annotations + +from sklearn.preprocessing import StandardScaler as sk_StandardScaler + +from safeds.data.tabular.containers import Table +from safeds.data.tabular.transformation._table_transformer import InvertibleTableTransformer +from safeds.exceptions import NonNumericColumnError, TransformerNotFittedError, UnknownColumnNameError + + +class StandardScaler(InvertibleTableTransformer): + """The StandardScaler transforms column values to a range by removing the mean and scaling to unit variance.""" + + def __init__(self) -> None: + self._column_names: list[str] | None = None + self._wrapped_transformer: sk_StandardScaler | None = None + + def fit(self, table: Table, column_names: list[str] | None) -> StandardScaler: + """ + Learn a transformation for a set of columns in a table. + + This transformer is not modified. + + Parameters + ---------- + table : Table + The table used to fit the transformer. + column_names : Optional[list[str]] + The list of columns from the table used to fit the transformer. If `None`, all columns are used. + + Returns + ------- + fitted_transformer : TableTransformer + The fitted transformer. + + Raises + ------ + UnknownColumnNameError + If column_names contain a column name that is missing in the table + NonNumericColumnError + If at least one of the specified columns in the table contains non-numerical data + ValueError + If the table contains 0 rows + """ + if column_names is None: + column_names = table.column_names + else: + missing_columns = sorted(set(column_names) - set(table.column_names)) + if len(missing_columns) > 0: + raise UnknownColumnNameError(missing_columns) + + if ( + table.keep_only_columns(column_names).remove_columns_with_non_numerical_values().number_of_columns + < table.keep_only_columns(column_names).number_of_columns + ): + raise NonNumericColumnError( + str( + sorted( + set(table.keep_only_columns(column_names).column_names) + - set( + table.keep_only_columns(column_names) + .remove_columns_with_non_numerical_values() + .column_names, + ), + ), + ), + ) + + if table.number_of_rows == 0: + raise ValueError("The StandardScaler cannot be fitted because the table contains 0 rows") + + wrapped_transformer = sk_StandardScaler() + wrapped_transformer.fit(table._data[column_names]) + + result = StandardScaler() + result._wrapped_transformer = wrapped_transformer + result._column_names = column_names + + return result + + def transform(self, table: Table) -> Table: + """ + Apply the learned transformation to a table. + + The table is not modified. + + Parameters + ---------- + table : Table + The table to which the learned transformation is applied. + + Returns + ------- + transformed_table : Table + The transformed table. + + Raises + ------ + TransformerNotFittedError + If the transformer has not been fitted yet. + UnknownColumnNameError + If the input table does not contain all columns used to fit the transformer + NonNumericColumnError + If at least one of the columns in the input table that is used to fit contains non-numerical data + ValueError + If the table contains 0 rows + """ + # Transformer has not been fitted yet + if self._wrapped_transformer is None or self._column_names is None: + raise TransformerNotFittedError + + # Input table does not contain all columns used to fit the transformer + missing_columns = sorted(set(self._column_names) - set(table.column_names)) + if len(missing_columns) > 0: + raise UnknownColumnNameError(missing_columns) + + if ( + table.keep_only_columns(self._column_names).remove_columns_with_non_numerical_values().number_of_columns + < table.keep_only_columns(self._column_names).number_of_columns + ): + raise NonNumericColumnError( + str( + sorted( + set(table.keep_only_columns(self._column_names).column_names) + - set( + table.keep_only_columns(self._column_names) + .remove_columns_with_non_numerical_values() + .column_names, + ), + ), + ), + ) + + if table.number_of_rows == 0: + raise ValueError("The StandardScaler cannot transform the table because it contains 0 rows") + + data = table._data.copy() + data.columns = table.column_names + data[self._column_names] = self._wrapped_transformer.transform(data[self._column_names]) + return Table._from_pandas_dataframe(data) + + def inverse_transform(self, transformed_table: Table) -> Table: + """ + Undo the learned transformation. + + The table is not modified. + + Parameters + ---------- + transformed_table : Table + The table to be transformed back to the original version. + + Returns + ------- + table : Table + The original table. + + Raises + ------ + TransformerNotFittedError + If the transformer has not been fitted yet. + UnknownColumnNameError + If the input table does not contain all columns used to fit the transformer + NonNumericColumnError + If the transformed columns of the input table contain non-numerical data + ValueError + If the table contains 0 rows + """ + # Transformer has not been fitted yet + if self._wrapped_transformer is None or self._column_names is None: + raise TransformerNotFittedError + + missing_columns = sorted(set(self._column_names) - set(transformed_table.column_names)) + if len(missing_columns) > 0: + raise UnknownColumnNameError(missing_columns) + + if ( + transformed_table.keep_only_columns(self._column_names) + .remove_columns_with_non_numerical_values() + .number_of_columns + < transformed_table.keep_only_columns(self._column_names).number_of_columns + ): + raise NonNumericColumnError( + str( + sorted( + set(transformed_table.keep_only_columns(self._column_names).column_names) + - set( + transformed_table.keep_only_columns(self._column_names) + .remove_columns_with_non_numerical_values() + .column_names, + ), + ), + ), + ) + + if transformed_table.number_of_rows == 0: + raise ValueError("The StandardScaler cannot transform the table because it contains 0 rows") + + data = transformed_table._data.copy() + data.columns = transformed_table.column_names + data[self._column_names] = self._wrapped_transformer.inverse_transform(data[self._column_names]) + return Table._from_pandas_dataframe(data) + + def is_fitted(self) -> bool: + """ + Check if the transformer is fitted. + + Returns + ------- + is_fitted : bool + Whether the transformer is fitted. + """ + return self._wrapped_transformer is not None + + def get_names_of_added_columns(self) -> list[str]: + """ + Get the names of all new columns that have been added by the StandardScaler. + + Returns + ------- + added_columns : list[str] + A list of names of the added columns, ordered as they will appear in the table. + + Raises + ------ + TransformerNotFittedError + If the transformer has not been fitted yet. + """ + if not self.is_fitted(): + raise TransformerNotFittedError + return [] + + # (Must implement abstract method, cannot instantiate class otherwise.) + def get_names_of_changed_columns(self) -> list[str]: + """ + Get the names of all columns that may have been changed by the StandardScaler. + + Returns + ------- + changed_columns : list[str] + The list of (potentially) changed column names, as passed to fit. + + Raises + ------ + TransformerNotFittedError + If the transformer has not been fitted yet. + """ + if self._column_names is None: + raise TransformerNotFittedError + return self._column_names + + def get_names_of_removed_columns(self) -> list[str]: + """ + Get the names of all columns that have been removed by the StandardScaler. + + Returns + ------- + removed_columns : list[str] + A list of names of the removed columns, ordered as they appear in the table the StandardScaler was fitted on. + + Raises + ------ + TransformerNotFittedError + If the transformer has not been fitted yet. + """ + if not self.is_fitted(): + raise TransformerNotFittedError + return [] diff --git a/src/safeds/data/tabular/transformation/_table_transformer.py b/src/safeds/data/tabular/transformation/_table_transformer.py index dac2f5072..cd9c75ecb 100644 --- a/src/safeds/data/tabular/transformation/_table_transformer.py +++ b/src/safeds/data/tabular/transformation/_table_transformer.py @@ -11,10 +11,12 @@ class TableTransformer(ABC): """Learn a transformation for a set of columns in a `Table` and transform another `Table` with the same columns.""" @abstractmethod - def fit(self, table: Table, column_names: list[str] | None = None) -> TableTransformer: + def fit(self, table: Table, column_names: list[str] | None) -> TableTransformer: """ Learn a transformation for a set of columns in a table. + This transformer is not modified. + Parameters ---------- table : Table @@ -33,6 +35,8 @@ def transform(self, table: Table) -> Table: """ Apply the learned transformation to a table. + The table is not modified. + Parameters ---------- table : Table @@ -49,6 +53,54 @@ def transform(self, table: Table) -> Table: If the transformer has not been fitted yet. """ + @abstractmethod + def get_names_of_added_columns(self) -> list[str]: + """ + Get the names of all new columns that have been added by the transformer. + + Returns + ------- + added_columns : list[str] + A list of names of the added columns, ordered as they will appear in the table. + + Raises + ------ + TransformerNotFittedError + If the transformer has not been fitted yet. + """ + + @abstractmethod + def get_names_of_changed_columns(self) -> list[str]: + """ + Get the names of all columns that have been changed by the transformer. + + Returns + ------- + changed_columns : list[str] + A list of names of changed columns, ordered as they appear in the table. + + Raises + ------ + TransformerNotFittedError + If the transformer has not been fitted yet. + """ + + @abstractmethod + def get_names_of_removed_columns(self) -> list[str]: + """ + Get the names of all columns that have been removed by the transformer. + + Returns + ------- + removed_columns : list[str] + A list of names of the removed columns, ordered as they appear in the table the transformer was fitted on. + + Raises + ------ + TransformerNotFittedError + If the transformer has not been fitted yet. + """ + @abstractmethod def is_fitted(self) -> bool: """ @@ -64,7 +116,7 @@ def fit_and_transform(self, table: Table, column_names: list[str] | None = None) """ Learn a transformation for a set of columns in a table and apply the learned transformation to the same table. - If you also need the fitted transformer, use `fit` and `transform` separately. + The table is not modified. If you also need the fitted transformer, use `fit` and `transform` separately. Parameters ---------- @@ -84,11 +136,31 @@ def fit_and_transform(self, table: Table, column_names: list[str] | None = None) class InvertibleTableTransformer(TableTransformer): """A `TableTransformer` that can also undo the learned transformation after it has been applied.""" + @abstractmethod + def fit(self, table: Table, column_names: list[str] | None) -> InvertibleTableTransformer: + """ + Learn a transformation for a set of columns in a table. + + Parameters + ---------- + table : Table + The table used to fit the transformer. + column_names : Optional[list[str]] + The list of columns from the table used to fit the transformer. If `None`, all columns are used. + + Returns + ------- + fitted_transformer : InvertibleTableTransformer + The fitted transformer. + """ + @abstractmethod def inverse_transform(self, transformed_table: Table) -> Table: """ Undo the learned transformation. + The table is not modified. + Parameters ---------- transformed_table : Table diff --git a/src/safeds/data/tabular/typing/_column_type.py b/src/safeds/data/tabular/typing/_column_type.py index 8b2a3f7a1..82396f334 100644 --- a/src/safeds/data/tabular/typing/_column_type.py +++ b/src/safeds/data/tabular/typing/_column_type.py @@ -11,6 +11,38 @@ class ColumnType(ABC): """Abstract base class for column types.""" + @staticmethod + def _from_numpy_data_type(data_type: np.dtype) -> ColumnType: + """ + Return the column type for a given `numpy` data type. + + Parameters + ---------- + data_type : numpy.dtype + The `numpy` data type. + + Returns + ------- + column_type : ColumnType + The ColumnType. + + Raises + ------ + NotImplementedError + If the given data type is not supported. + """ + if data_type.kind in ("u", "i"): + return Integer() + if data_type.kind == "b": + return Boolean() + if data_type.kind == "f": + return RealNumber() + if data_type.kind in ("S", "U", "O", "M", "m"): + return String() + + message = f"Unsupported numpy data type '{data_type}'." + raise NotImplementedError(message) + @abstractmethod def is_nullable(self) -> bool: """ @@ -33,36 +65,6 @@ def is_numeric(self) -> bool: True if the column is numeric. """ - @staticmethod - def _from_numpy_dtype(dtype: np.dtype) -> ColumnType: - """ - Return the column type for a given numpy dtype. - - Parameters - ---------- - dtype : numpy.dtype - The numpy dtype. - - Returns - ------- - column_type : ColumnType - The ColumnType. - - Raises - ------ - TypeError - If the given dtype is not supported. - """ - if dtype.kind in ("u", "i"): - return Integer() - if dtype.kind == "b": - return Boolean() - if dtype.kind == "f": - return RealNumber() - if dtype.kind in ("S", "U", "O", "M", "m"): - return String() - raise TypeError("Unexpected column type") - @dataclass class Anything(ColumnType): diff --git a/src/safeds/data/tabular/typing/_imputer_strategy.py b/src/safeds/data/tabular/typing/_imputer_strategy.py index 47315274c..fd55477c5 100644 --- a/src/safeds/data/tabular/typing/_imputer_strategy.py +++ b/src/safeds/data/tabular/typing/_imputer_strategy.py @@ -12,4 +12,11 @@ class ImputerStrategy(ABC): @abstractmethod def _augment_imputer(self, imputer: sk_SimpleImputer) -> None: - pass + """ + Set the imputer strategy of the given imputer. + + Parameters + ---------- + imputer: SimpleImputer + The imputer to augment. + """ diff --git a/src/safeds/data/tabular/typing/_schema.py b/src/safeds/data/tabular/typing/_schema.py index 135d709dd..a75a87241 100644 --- a/src/safeds/data/tabular/typing/_schema.py +++ b/src/safeds/data/tabular/typing/_schema.py @@ -3,8 +3,8 @@ from dataclasses import dataclass from typing import TYPE_CHECKING -from safeds.data.tabular.exceptions import UnknownColumnNameError from safeds.data.tabular.typing._column_type import ColumnType +from safeds.exceptions import UnknownColumnNameError if TYPE_CHECKING: import pandas as pd @@ -19,13 +19,129 @@ class Schema: ---------- schema : dict[str, ColumnType] Map from column names to data types. + + Examples + -------- + >>> from safeds.data.tabular.typing import Integer, Schema, String + >>> schema = Schema({"A": Integer(), "B": String()}) """ _schema: dict[str, ColumnType] + # ------------------------------------------------------------------------------------------------------------------ + # Creation + # ------------------------------------------------------------------------------------------------------------------ + + @staticmethod + def _from_pandas_dataframe(dataframe: pd.DataFrame) -> Schema: + """ + Create a schema from a `pandas.DataFrame`. + + Parameters + ---------- + dataframe : pd.DataFrame + The dataframe. + + Returns + ------- + schema : Schema + The schema. + """ + names = dataframe.columns + # noinspection PyProtectedMember + types = (ColumnType._from_numpy_data_type(data_type) for data_type in dataframe.dtypes) + + return Schema(dict(zip(names, types, strict=True))) + + # ------------------------------------------------------------------------------------------------------------------ + # Dunder methods + # ------------------------------------------------------------------------------------------------------------------ + def __init__(self, schema: dict[str, ColumnType]): self._schema = dict(schema) # Defensive copy + def __hash__(self) -> int: + """ + Return a hash value for the schema. + + Returns + ------- + hash : int + The hash value. + + Examples + -------- + >>> from safeds.data.tabular.typing import Integer, Schema, String + >>> schema = Schema({"A": Integer(), "B": String()}) + >>> hash_value = hash(schema) + """ + column_names = self._schema.keys() + column_types = map(repr, self._schema.values()) + return hash(tuple(zip(column_names, column_types, strict=True))) + + def __repr__(self) -> str: + """ + Return an unambiguous string representation of this row. + + Returns + ------- + representation : str + The string representation. + + Examples + -------- + >>> from safeds.data.tabular.typing import Integer, Schema, String + >>> schema = Schema({"A": Integer()}) + >>> repr(schema) + "Schema({'A': Integer})" + """ + return f"Schema({str(self)})" + + def __str__(self) -> str: + """ + Return a user-friendly string representation of the schema. + + Returns + ------- + string : str + The string representation. + + Examples + -------- + >>> from safeds.data.tabular.typing import Integer, Schema, String + >>> schema = Schema({"A": Integer()}) + >>> str(schema) + "{'A': Integer}" + """ + match len(self._schema): + case 0: + return "{}" + case 1: + return str(self._schema) + case _: + lines = (f" {name!r}: {type_}" for name, type_ in self._schema.items()) + joined = ",\n".join(lines) + return f"{{\n{joined}\n}}" + + @property + def column_names(self) -> list[str]: + """ + Return a list of all column names saved in this schema. + + Returns + ------- + column_names : list[str] + The column names. + + Examples + -------- + >>> from safeds.data.tabular.typing import Integer, Schema, String + >>> schema = Schema({"A": Integer(), "B": String()}) + >>> schema.column_names + ['A', 'B'] + """ + return list(self._schema.keys()) + def has_column(self, column_name: str) -> bool: """ Return whether the schema contains a given column. @@ -39,10 +155,20 @@ def has_column(self, column_name: str) -> bool: ------- contains : bool True if the schema contains the column. + + Examples + -------- + >>> from safeds.data.tabular.typing import Integer, Schema, String + >>> schema = Schema({"A": Integer(), "B": String()}) + >>> schema.has_column("A") + True + + >>> schema.has_column("C") + False """ return column_name in self._schema - def get_type_of_column(self, column_name: str) -> ColumnType: + def get_column_type(self, column_name: str) -> ColumnType: """ Return the type of the given column. @@ -58,83 +184,58 @@ def get_type_of_column(self, column_name: str) -> ColumnType: Raises ------ - ColumnNameError - If the specified target column name does not exist. + UnknownColumnNameError + If the specified column name does not exist. + + Examples + -------- + >>> from safeds.data.tabular.typing import Integer, Schema, String + >>> schema = Schema({"A": Integer(), "B": String()}) + >>> schema.get_column_type("A") + Integer """ if not self.has_column(column_name): raise UnknownColumnNameError([column_name]) return self._schema[column_name] - def _get_column_index_by_name(self, column_name: str) -> int: - """ - Return the index of the column with specified column name. - - Parameters - ---------- - column_name : str - The name of the column. - - Returns - ------- - index : int - The index of the column. - """ - return list(self._schema.keys()).index(column_name) + # ------------------------------------------------------------------------------------------------------------------ + # Conversion + # ------------------------------------------------------------------------------------------------------------------ - @staticmethod - def _from_dataframe(dataframe: pd.DataFrame) -> Schema: + def to_dict(self) -> dict[str, ColumnType]: """ - Construct a TableSchema from a Dataframe. This function is not supposed to be exposed to the user. - - Parameters - ---------- - dataframe : pd.DataFrame - The Dataframe used to construct the TableSchema. + Return a dictionary that maps column names to column types. Returns ------- - _from_dataframe: Schema - The constructed TableSchema. - + data : dict[str, ColumnType] + Dictionary representation of the schema. + + Examples + -------- + >>> from safeds.data.tabular.typing import Integer, Schema, String + >>> schema = Schema({"A": Integer(), "B": String()}) + >>> schema.to_dict() + {'A': Integer, 'B': String} """ - names = dataframe.columns - # noinspection PyProtectedMember - types = (ColumnType._from_numpy_dtype(dtype) for dtype in dataframe.dtypes) + return dict(self._schema) # defensive copy - return Schema(dict(zip(names, types, strict=True))) + # ------------------------------------------------------------------------------------------------------------------ + # IPython Integration + # ------------------------------------------------------------------------------------------------------------------ - def get_column_names(self) -> list[str]: + def _repr_markdown_(self) -> str: """ - Return a list of all column names saved in this schema. + Return a Markdown representation of the schema. Returns ------- - column_names : list[str] - The column names. + markdown : str + The Markdown representation. """ - return list(self._schema.keys()) + if len(self._schema) == 0: + return "Empty Schema" - def __str__(self) -> str: - """ - Return a print-string for the TableSchema. - - Returns - ------- - output_string : str - The string. - """ - column_count = len(self._schema) - output_string = f"TableSchema:\nColumn Count: {column_count}\nColumns:\n" - for column_name, data_type in self._schema.items(): - output_string += f" {column_name}: {data_type}\n" - return output_string - - def __repr__(self) -> str: - return self.__str__() - - def __eq__(self, o: object) -> bool: - if not isinstance(o, Schema): - return NotImplemented - if self is o: - return True - return self._schema == o._schema + lines = (f"| {name} | {type_} |" for name, type_ in self._schema.items()) + joined = "\n".join(lines) + return f"| Column Name | Column Type |\n| --- | --- |\n{joined}" diff --git a/src/safeds/exceptions/__init__.py b/src/safeds/exceptions/__init__.py new file mode 100644 index 000000000..cf9b10066 --- /dev/null +++ b/src/safeds/exceptions/__init__.py @@ -0,0 +1,51 @@ +"""Custom exceptions that can be raised by Safe-DS.""" + +from safeds.exceptions._data import ( + ColumnIsTargetError, + ColumnLengthMismatchError, + ColumnSizeError, + DuplicateColumnNameError, + IllegalSchemaModificationError, + IndexOutOfBoundsError, + MissingValuesColumnError, + NonNumericColumnError, + SchemaMismatchError, + TransformerNotFittedError, + UnknownColumnNameError, + ValueNotPresentWhenFittedError, + WrongFileExtensionError, +) +from safeds.exceptions._ml import ( + DatasetContainsTargetError, + DatasetMissesDataError, + DatasetMissesFeaturesError, + LearningError, + ModelNotFittedError, + PredictionError, + UntaggedTableError, +) + +__all__ = [ + # Data exceptions + "ColumnIsTargetError", + "ColumnLengthMismatchError", + "ColumnSizeError", + "DuplicateColumnNameError", + "IllegalSchemaModificationError", + "IndexOutOfBoundsError", + "MissingValuesColumnError", + "NonNumericColumnError", + "SchemaMismatchError", + "TransformerNotFittedError", + "UnknownColumnNameError", + "ValueNotPresentWhenFittedError", + "WrongFileExtensionError", + # ML exceptions + "DatasetContainsTargetError", + "DatasetMissesDataError", + "DatasetMissesFeaturesError", + "LearningError", + "ModelNotFittedError", + "PredictionError", + "UntaggedTableError", +] diff --git a/src/safeds/exceptions/_data.py b/src/safeds/exceptions/_data.py new file mode 100644 index 000000000..f11c7a334 --- /dev/null +++ b/src/safeds/exceptions/_data.py @@ -0,0 +1,154 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from pathlib import Path + + +class UnknownColumnNameError(KeyError): + """ + Exception raised for trying to access an invalid column name. + + Parameters + ---------- + column_names : list[str] + The name of the column that was tried to be accessed. + """ + + def __init__(self, column_names: list[str]): + super().__init__(f"Could not find column(s) '{', '.join(column_names)}'") + + +class NonNumericColumnError(Exception): + """Exception raised for trying to do numerical operations on a non-numerical column.""" + + def __init__(self, column_info: str, help_msg: str | None = None) -> None: + line_break = "\n" + super().__init__( + ( + "Tried to do a numerical operation on one or multiple non-numerical columns:" + f" \n{column_info}{line_break + help_msg if help_msg is not None else ''}" + ), + ) + + +class MissingValuesColumnError(Exception): + """Exception raised for trying to do operations on columns containing missing values.""" + + def __init__(self, column_info: str, help_msg: str | None = None) -> None: + line_break = "\n" + super().__init__( + ( + "Tried to do an operation on one or multiple columns containing missing values:" + f" \n{column_info}{line_break + help_msg if help_msg is not None else ''}" + ), + ) + + +class DuplicateColumnNameError(Exception): + """ + Exception raised for trying to modify a table resulting in a duplicate column name. + + Parameters + ---------- + column_name : str + The name of the column that resulted in a duplicate. + """ + + def __init__(self, column_name: str): + super().__init__(f"Column '{column_name}' already exists.") + + +class IndexOutOfBoundsError(IndexError): + """ + Exception raised for trying to access an element by an index that does not exist in the underlying data. + + Parameters + ---------- + index : int | slice + The wrongly used index. + """ + + def __init__(self, index: int | slice): + if isinstance(index, int): + super().__init__(f"There is no element at index '{index}'.") + else: + super().__init__(f"There is no element in the range [{index.start}, {index.stop}]") + + +class ColumnSizeError(Exception): + """ + Exception raised for trying to use a column of unsupported size. + + Parameters + ---------- + expected_size : str + The expected size of the column as an expression (e.g. 2, >0, !=0). + actual_size : str + The actual size of the column as an expression (e.g. 2, >0, !=0). + """ + + def __init__(self, expected_size: str, actual_size: str): + super().__init__(f"Expected a column of size {expected_size} but got column of size {actual_size}.") + + +class SchemaMismatchError(Exception): + """Exception raised when schemas are unequal.""" + + def __init__(self) -> None: + super().__init__("Failed because at least two schemas didn't match.") + + +class ColumnLengthMismatchError(Exception): + """Exception raised when the lengths of two or more columns do not match.""" + + def __init__(self, column_info: str): + super().__init__(f"The length of at least one column differs: \n{column_info}") + + +class TransformerNotFittedError(Exception): + """Raised when a transformer is used before fitting it.""" + + def __init__(self) -> None: + super().__init__("The transformer has not been fitted yet.") + + +class ValueNotPresentWhenFittedError(Exception): + """Exception raised when attempting to one-hot-encode a table containing values not present in the fitting phase.""" + + def __init__(self, values: list[tuple[str, str]]) -> None: + values_info = [f"{value} in column {column}" for value, column in values] + line_break = "\n" + super().__init__( + ( + "Value(s) not present in the table the transformer was fitted on:" + f" {line_break}{line_break.join(values_info)}" + ), + ) + + +class WrongFileExtensionError(Exception): + """Exception raised when the file has the wrong file extension.""" + + def __init__(self, file: str | Path, file_extension: str | list[str]) -> None: + super().__init__( + ( + f"The file {file} has a wrong file extension. Please provide a file with the following extension(s):" + f" {file_extension}" + ), + ) + + +class IllegalSchemaModificationError(Exception): + """Exception raised when modifying a schema in a way that is inconsistent with the subclass's requirements.""" + + def __init__(self, msg: str) -> None: + super().__init__(f"Illegal schema modification: {msg}") + + +class ColumnIsTargetError(IllegalSchemaModificationError): + """Exception raised in overriden methods of the Table class when removing tagged Columns from a TaggedTable.""" + + def __init__(self, column_name: str) -> None: + super().__init__(f'Column "{column_name}" is the target column and cannot be removed.') diff --git a/src/safeds/ml/exceptions/_exceptions.py b/src/safeds/exceptions/_ml.py similarity index 58% rename from src/safeds/ml/exceptions/_exceptions.py rename to src/safeds/exceptions/_ml.py index 7b07db8f2..1cc83ebc0 100644 --- a/src/safeds/ml/exceptions/_exceptions.py +++ b/src/safeds/exceptions/_ml.py @@ -26,21 +26,25 @@ def __init__(self, missing_feature_names: list[str]): super().__init__(f"Dataset misses the feature columns '{missing_feature_names}'.") +class DatasetMissesDataError(ValueError): + """Raised when a dataset contains no rows.""" + + def __init__(self) -> None: + super().__init__("Dataset contains no rows") + + class LearningError(Exception): """ Raised when an error occurred while training a model. Parameters ---------- - reason: str | None + reason: str The reason for the error. """ - def __init__(self, reason: str | None): - if reason is None: - super().__init__("Error occurred while learning") - else: - super().__init__(f"Error occurred while learning: {reason}") + def __init__(self, reason: str): + super().__init__(f"Error occurred while learning: {reason}") class ModelNotFittedError(Exception): @@ -56,12 +60,21 @@ class PredictionError(Exception): Parameters ---------- - reason: str | None + reason: str The reason for the error. """ - def __init__(self, reason: str | None): - if reason is None: - super().__init__("Error occurred while predicting") - else: - super().__init__(f"Error occurred while predicting: {reason}") + def __init__(self, reason: str): + super().__init__(f"Error occurred while predicting: {reason}") + + +class UntaggedTableError(Exception): + """Raised when an untagged table is used instead of a TaggedTable in a regression or classification.""" + + def __init__(self) -> None: + super().__init__( + ( + "This method needs a tagged table.\nA tagged table is a table that additionally knows which columns are" + " features and which are the target to predict.\nUse Table.tag_column() to create a tagged table." + ), + ) diff --git a/src/safeds/ml/_util_sklearn.py b/src/safeds/ml/_util_sklearn.py deleted file mode 100644 index ea81efb3e..000000000 --- a/src/safeds/ml/_util_sklearn.py +++ /dev/null @@ -1,91 +0,0 @@ -from typing import Any - -from safeds.data.tabular.containers import Table, TaggedTable -from safeds.ml.exceptions import ( - DatasetContainsTargetError, - DatasetMissesFeaturesError, - LearningError, - ModelNotFittedError, - PredictionError, -) - - -# noinspection PyProtectedMember -def fit(model: Any, tagged_table: TaggedTable) -> None: - """ - Fit a model for a given tagged table. - - Parameters - ---------- - model - Classifier or Regression from scikit-learn. - tagged_table : TaggedTable - The tagged table containing the feature and target vectors. - - Raises - ------ - LearningError - If the tagged table contains invalid values or if the training failed. - """ - try: - model.fit( - tagged_table.features._data, - tagged_table.target._data, - ) - except ValueError as exception: - raise LearningError(str(exception)) from exception - - -# noinspection PyProtectedMember -def predict(model: Any, dataset: Table, feature_names: list[str] | None, target_name: str | None) -> TaggedTable: - """ - Predict a target vector using a dataset containing feature vectors. The model has to be trained first. - - Parameters - ---------- - model - Classifier or regressor from scikit-learn. - dataset : Table - The dataset containing the features. - target_name : Optional[str] - The name of the target column. - feature_names : Optional[list[str]] - The names of the feature columns. - - Returns - ------- - table : TaggedTable - A dataset containing the given features and the predicted target. - - Raises - ------ - ModelNotFittedError - If the model has not been fitted yet. - DatasetContainsTargetError - If the dataset contains the target column already. - DatasetMissesFeaturesError - If the dataset misses feature columns. - PredictionError - If predicting with the given dataset failed. - """ - # Validation - if model is None or target_name is None or feature_names is None: - raise ModelNotFittedError - if dataset.has_column(target_name): - raise DatasetContainsTargetError(target_name) - missing_feature_names = [feature_name for feature_name in feature_names if not dataset.has_column(feature_name)] - if missing_feature_names: - raise DatasetMissesFeaturesError(missing_feature_names) - - dataset_df = dataset.keep_only_columns(feature_names)._data - dataset_df.columns = feature_names - - result_set = dataset._data.copy(deep=True) - result_set.columns = dataset.get_column_names() - - try: - predicted_target_vector = model.predict(dataset_df.values) - result_set[target_name] = predicted_target_vector - return Table(result_set).tag_columns(target_name=target_name, feature_names=feature_names) - except ValueError as exception: - raise PredictionError(str(exception)) from exception diff --git a/src/safeds/ml/classical/__init__.py b/src/safeds/ml/classical/__init__.py new file mode 100644 index 000000000..720ab83bb --- /dev/null +++ b/src/safeds/ml/classical/__init__.py @@ -0,0 +1 @@ +"""Classes for classical machine learning, i.e. machine learning that does not use neural networks.""" diff --git a/src/safeds/ml/classical/_util_sklearn.py b/src/safeds/ml/classical/_util_sklearn.py new file mode 100644 index 000000000..8cd2c0229 --- /dev/null +++ b/src/safeds/ml/classical/_util_sklearn.py @@ -0,0 +1,176 @@ +import warnings +from typing import Any + +from safeds.data.tabular.containers import Table, TaggedTable +from safeds.exceptions import ( + DatasetContainsTargetError, + DatasetMissesDataError, + DatasetMissesFeaturesError, + LearningError, + MissingValuesColumnError, + ModelNotFittedError, + NonNumericColumnError, + PredictionError, + UntaggedTableError, +) + + +# noinspection PyProtectedMember +def fit(model: Any, tagged_table: TaggedTable) -> None: + """ + Fit a model for a given tagged table. + + Parameters + ---------- + model + Classifier or Regression from scikit-learn. + tagged_table : TaggedTable + The tagged table containing the feature and target vectors. + + Raises + ------ + LearningError + If the tagged table contains invalid values or if the training failed. + UntaggedTableError + If the table is untagged. + NonNumericColumnError + If the training data contains non-numerical values. + MissingValuesColumnError + If the training data contains missing values. + DatasetMissesDataError + If the training data contains no rows. + """ + if not isinstance(tagged_table, TaggedTable) and isinstance(tagged_table, Table): + raise UntaggedTableError + + if tagged_table.number_of_rows == 0: + raise DatasetMissesDataError + + non_numerical_column_names = set(tagged_table.features.column_names) - set( + tagged_table.features.remove_columns_with_non_numerical_values().column_names, + ) + if len(non_numerical_column_names) != 0: + raise NonNumericColumnError( + str(non_numerical_column_names), + ( + "You can use the LabelEncoder or OneHotEncoder to transform your non-numerical data to numerical" + " data.\nThe OneHotEncoder should be used if you work with nominal data. If your data contains too many" + " different values\nor is ordinal, you should use the LabelEncoder." + ), + ) + + null_containing_column_names = set(tagged_table.features.column_names) - set( + tagged_table.features.remove_columns_with_missing_values().column_names, + ) + if len(null_containing_column_names) != 0: + raise MissingValuesColumnError( + str(null_containing_column_names), + ( + "You can use the Imputer to replace the missing values based on different strategies.\nIf you want to" + " remove the missing values entirely you can use the method `Table.remove_rows_with_missing_values`." + ), + ) + + try: + model.fit( + tagged_table.features._data, + tagged_table.target._data, + ) + except ValueError as exception: + raise LearningError(str(exception)) from exception + + +# noinspection PyProtectedMember +def predict(model: Any, dataset: Table, feature_names: list[str] | None, target_name: str | None) -> TaggedTable: + """ + Predict a target vector using a dataset containing feature vectors. The model has to be trained first. + + Parameters + ---------- + model + Classifier or regressor from scikit-learn. + dataset : Table + The dataset containing the features. + target_name : Optional[str] + The name of the target column. + feature_names : Optional[list[str]] + The names of the feature columns. + + Returns + ------- + table : TaggedTable + A dataset containing the given features and the predicted target. + + Raises + ------ + ModelNotFittedError + If the model has not been fitted yet. + DatasetContainsTargetError + If the dataset contains the target column already. + DatasetMissesFeaturesError + If the dataset misses feature columns. + PredictionError + If predicting with the given dataset failed. + NonNumericColumnError + If the dataset contains non-numerical values. + MissingValuesColumnError + If the dataset contains missing values. + DatasetMissesDataError + If the dataset contains no rows. + """ + # Validation + if model is None or target_name is None or feature_names is None: + raise ModelNotFittedError + if dataset.has_column(target_name): + raise DatasetContainsTargetError(target_name) + missing_feature_names = [feature_name for feature_name in feature_names if not dataset.has_column(feature_name)] + if missing_feature_names: + raise DatasetMissesFeaturesError(missing_feature_names) + if isinstance(dataset, TaggedTable): + dataset = dataset.features # Cast to Table type, so Python will call the right methods... + + if dataset.number_of_rows == 0: + raise DatasetMissesDataError + + non_numerical_column_names = set(dataset.keep_only_columns(feature_names).column_names) - set( + dataset.keep_only_columns(feature_names).remove_columns_with_non_numerical_values().column_names, + ) + if len(non_numerical_column_names) != 0: + raise NonNumericColumnError( + str(non_numerical_column_names), + ( + "You can use the LabelEncoder or OneHotEncoder to transform your non-numerical data to numerical" + " data.\nThe OneHotEncoder should be used if you work with nominal data. If your data contains too many" + " different values\nor is ordinal, you should use the LabelEncoder.\n" + ), + ) + + null_containing_column_names = set(dataset.keep_only_columns(feature_names).column_names) - set( + dataset.keep_only_columns(feature_names).remove_columns_with_missing_values().column_names, + ) + if len(null_containing_column_names) != 0: + raise MissingValuesColumnError( + str(null_containing_column_names), + ( + "You can use the Imputer to replace the missing values based on different strategies.\nIf you want to" + " remove the missing values entirely you can use the method `Table.remove_rows_with_missing_values`." + ), + ) + + dataset_df = dataset.keep_only_columns(feature_names)._data + dataset_df.columns = feature_names + + result_set = dataset._data.copy(deep=True) + result_set.columns = dataset.column_names + + try: + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", message="X does not have valid feature names") + predicted_target_vector = model.predict(dataset_df.values) + result_set[target_name] = predicted_target_vector + return Table._from_pandas_dataframe(result_set).tag_columns( + target_name=target_name, + feature_names=feature_names, + ) + except ValueError as exception: + raise PredictionError(str(exception)) from exception diff --git a/src/safeds/ml/classification/__init__.py b/src/safeds/ml/classical/classification/__init__.py similarity index 77% rename from src/safeds/ml/classification/__init__.py rename to src/safeds/ml/classical/classification/__init__.py index dcae90c57..7871a7bde 100644 --- a/src/safeds/ml/classification/__init__.py +++ b/src/safeds/ml/classical/classification/__init__.py @@ -3,10 +3,11 @@ from ._ada_boost import AdaBoost from ._classifier import Classifier from ._decision_tree import DecisionTree -from ._gradient_boosting_classification import GradientBoosting +from ._gradient_boosting import GradientBoosting from ._k_nearest_neighbors import KNearestNeighbors from ._logistic_regression import LogisticRegression from ._random_forest import RandomForest +from ._support_vector_machine import SupportVectorMachine __all__ = [ "AdaBoost", @@ -16,4 +17,5 @@ "KNearestNeighbors", "LogisticRegression", "RandomForest", + "SupportVectorMachine", ] diff --git a/src/safeds/ml/classical/classification/_ada_boost.py b/src/safeds/ml/classical/classification/_ada_boost.py new file mode 100644 index 000000000..6cde8e4f2 --- /dev/null +++ b/src/safeds/ml/classical/classification/_ada_boost.py @@ -0,0 +1,198 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING + +from sklearn.ensemble import AdaBoostClassifier as sk_AdaBoostClassifier + +from safeds.ml.classical._util_sklearn import fit, predict + +from ._classifier import Classifier + +if TYPE_CHECKING: + from sklearn.base import ClassifierMixin + + from safeds.data.tabular.containers import Table, TaggedTable + + +class AdaBoost(Classifier): + """ + Ada Boost classification. + + Parameters + ---------- + learner: Classifier + The learner from which the boosted ensemble is built. + maximum_number_of_learners: int + The maximum number of learners at which boosting is terminated. In case of perfect fit, the learning procedure + is stopped early. Has to be greater than 0. + learning_rate : float + Weight applied to each classifier at each boosting iteration. A higher learning rate increases the contribution + of each classifier. Has to be greater than 0. + + Raises + ------ + ValueError + If `maximum_number_of_learners` or `learning_rate` are less than or equal to 0 + """ + + def __init__( + self, + *, + learner: Classifier | None = None, + maximum_number_of_learners: int = 50, + learning_rate: float = 1.0, + ) -> None: + # Validation + if maximum_number_of_learners <= 0: + raise ValueError("The parameter 'maximum_number_of_learners' has to be grater than 0.") + if learning_rate <= 0: + raise ValueError("The parameter 'learning_rate' has to be greater than 0.") + + # Hyperparameters + self._learner = learner + self._maximum_number_of_learners = maximum_number_of_learners + self._learning_rate = learning_rate + + # Internal state + self._wrapped_classifier: sk_AdaBoostClassifier | None = None + self._feature_names: list[str] | None = None + self._target_name: str | None = None + + @property + def learner(self) -> Classifier | None: + """ + Get the base learner used for training the ensemble. + + Returns + ------- + result: Classifier | None + The base learner. + """ + return self._learner + + @property + def maximum_number_of_learners(self) -> int: + """ + Get the maximum number of learners in the ensemble. + + Returns + ------- + result: int + The maximum number of learners. + """ + return self._maximum_number_of_learners + + @property + def learning_rate(self) -> float: + """ + Get the learning rate. + + Returns + ------- + result: float + The learning rate. + """ + return self._learning_rate + + def fit(self, training_set: TaggedTable) -> AdaBoost: + """ + Create a copy of this classifier and fit it with the given training data. + + This classifier is not modified. + + Parameters + ---------- + training_set : TaggedTable + The training data containing the feature and target vectors. + + Returns + ------- + fitted_classifier : AdaBoost + The fitted classifier. + + Raises + ------ + LearningError + If the training data contains invalid values or if the training failed. + UntaggedTableError + If the table is untagged. + NonNumericColumnError + If the training data contains non-numerical values. + MissingValuesColumnError + If the training data contains missing values. + DatasetMissesDataError + If the training data contains no rows. + """ + wrapped_classifier = self._get_sklearn_classifier() + fit(wrapped_classifier, training_set) + + result = AdaBoost( + learner=self.learner, + maximum_number_of_learners=self.maximum_number_of_learners, + learning_rate=self._learning_rate, + ) + result._wrapped_classifier = wrapped_classifier + result._feature_names = training_set.features.column_names + result._target_name = training_set.target.name + + return result + + def predict(self, dataset: Table) -> TaggedTable: + """ + Predict a target vector using a dataset containing feature vectors. The model has to be trained first. + + Parameters + ---------- + dataset : Table + The dataset containing the feature vectors. + + Returns + ------- + table : TaggedTable + A dataset containing the given feature vectors and the predicted target vector. + + Raises + ------ + ModelNotFittedError + If the model has not been fitted yet. + DatasetContainsTargetError + If the dataset contains the target column already. + DatasetMissesFeaturesError + If the dataset misses feature columns. + PredictionError + If predicting with the given dataset failed. + NonNumericColumnError + If the dataset contains non-numerical values. + MissingValuesColumnError + If the dataset contains missing values. + DatasetMissesDataError + If the dataset contains no rows. + """ + return predict(self._wrapped_classifier, dataset, self._feature_names, self._target_name) + + def is_fitted(self) -> bool: + """ + Check if the classifier is fitted. + + Returns + ------- + is_fitted : bool + Whether the classifier is fitted. + """ + return self._wrapped_classifier is not None + + def _get_sklearn_classifier(self) -> ClassifierMixin: + """ + Return a new wrapped Classifier from sklearn. + + Returns + ------- + wrapped_classifier: ClassifierMixin + The sklearn Classifier. + """ + learner = self.learner._get_sklearn_classifier() if self.learner is not None else None + return sk_AdaBoostClassifier( + estimator=learner, + n_estimators=self.maximum_number_of_learners, + learning_rate=self._learning_rate, + ) diff --git a/src/safeds/ml/classical/classification/_classifier.py b/src/safeds/ml/classical/classification/_classifier.py new file mode 100644 index 000000000..915ab8f3c --- /dev/null +++ b/src/safeds/ml/classical/classification/_classifier.py @@ -0,0 +1,232 @@ +from __future__ import annotations + +from abc import ABC, abstractmethod +from typing import TYPE_CHECKING + +from sklearn.metrics import accuracy_score as sk_accuracy_score + +from safeds.data.tabular.containers import Table, TaggedTable +from safeds.exceptions import UntaggedTableError + +if TYPE_CHECKING: + from typing import Any + + from sklearn.base import ClassifierMixin + + +class Classifier(ABC): + """Abstract base class for all classifiers.""" + + @abstractmethod + def fit(self, training_set: TaggedTable) -> Classifier: + """ + Create a copy of this classifier and fit it with the given training data. + + This classifier is not modified. + + Parameters + ---------- + training_set : TaggedTable + The training data containing the feature and target vectors. + + Returns + ------- + fitted_classifier : Classifier + The fitted classifier. + + Raises + ------ + LearningError + If the training data contains invalid values or if the training failed. + """ + + @abstractmethod + def predict(self, dataset: Table) -> TaggedTable: + """ + Predict a target vector using a dataset containing feature vectors. The model has to be trained first. + + Parameters + ---------- + dataset : Table + The dataset containing the feature vectors. + + Returns + ------- + table : TaggedTable + A dataset containing the given feature vectors and the predicted target vector. + + Raises + ------ + ModelNotFittedError + If the model has not been fitted yet. + DatasetContainsTargetError + If the dataset contains the target column already. + DatasetMissesFeaturesError + If the dataset misses feature columns. + PredictionError + If predicting with the given dataset failed. + """ + + @abstractmethod + def is_fitted(self) -> bool: + """ + Check if the classifier is fitted. + + Returns + ------- + is_fitted : bool + Whether the classifier is fitted. + """ + + @abstractmethod + def _get_sklearn_classifier(self) -> ClassifierMixin: + """ + Return a new wrapped Classifier from sklearn. + + Returns + ------- + wrapped_classifier: ClassifierMixin + The sklearn Classifier. + """ + + # noinspection PyProtectedMember + def accuracy(self, validation_or_test_set: TaggedTable) -> float: + """ + Compute the accuracy of the classifier on the given data. + + Parameters + ---------- + validation_or_test_set : TaggedTable + The validation or test set. + + Returns + ------- + accuracy : float + The calculated accuracy score, i.e. the percentage of equal data. + + Raises + ------ + UntaggedTableError + If the table is untagged. + """ + if not isinstance(validation_or_test_set, TaggedTable) and isinstance(validation_or_test_set, Table): + raise UntaggedTableError + + expected_values = validation_or_test_set.target + predicted_values = self.predict(validation_or_test_set.features).target + + return sk_accuracy_score(expected_values._data, predicted_values._data) + + def precision(self, validation_or_test_set: TaggedTable, positive_class: Any) -> float: + """ + Compute the classifier's precision on the given data. + + Parameters + ---------- + validation_or_test_set : TaggedTable + The validation or test set. + positive_class : Any + The class to be considered positive. All other classes are considered negative. + + Returns + ------- + precision : float + The calculated precision score, i.e. the ratio of correctly predicted positives to all predicted positives. + Return 1 if no positive predictions are made. + """ + if not isinstance(validation_or_test_set, TaggedTable) and isinstance(validation_or_test_set, Table): + raise UntaggedTableError + + expected_values = validation_or_test_set.target + predicted_values = self.predict(validation_or_test_set.features).target + + n_true_positives = 0 + n_false_positives = 0 + + for expected_value, predicted_value in zip(expected_values, predicted_values, strict=True): + if predicted_value == positive_class: + if expected_value == positive_class: + n_true_positives += 1 + else: + n_false_positives += 1 + + if (n_true_positives + n_false_positives) == 0: + return 1.0 + return n_true_positives / (n_true_positives + n_false_positives) + + def recall(self, validation_or_test_set: TaggedTable, positive_class: Any) -> float: + """ + Compute the classifier's recall on the given data. + + Parameters + ---------- + validation_or_test_set : TaggedTable + The validation or test set. + positive_class : Any + The class to be considered positive. All other classes are considered negative. + + Returns + ------- + recall : float + The calculated recall score, i.e. the ratio of correctly predicted positives to all expected positives. + Return 1 if there are no positive expectations. + """ + if not isinstance(validation_or_test_set, TaggedTable) and isinstance(validation_or_test_set, Table): + raise UntaggedTableError + + expected_values = validation_or_test_set.target + predicted_values = self.predict(validation_or_test_set.features).target + + n_true_positives = 0 + n_false_negatives = 0 + + for expected_value, predicted_value in zip(expected_values, predicted_values, strict=True): + if predicted_value == positive_class: + if expected_value == positive_class: + n_true_positives += 1 + elif expected_value == positive_class: + n_false_negatives += 1 + + if (n_true_positives + n_false_negatives) == 0: + return 1.0 + return n_true_positives / (n_true_positives + n_false_negatives) + + def f1_score(self, validation_or_test_set: TaggedTable, positive_class: Any) -> float: + """ + Compute the classifier's $F_1$-score on the given data. + + Parameters + ---------- + validation_or_test_set : TaggedTable + The validation or test set. + positive_class : Any + The class to be considered positive. All other classes are considered negative. + + Returns + ------- + f1_score : float + The calculated $F_1$-score, i.e. the harmonic mean between precision and recall. + Return 1 if there are no positive expectations and predictions. + """ + if not isinstance(validation_or_test_set, TaggedTable) and isinstance(validation_or_test_set, Table): + raise UntaggedTableError + + expected_values = validation_or_test_set.target + predicted_values = self.predict(validation_or_test_set.features).target + + n_true_positives = 0 + n_false_negatives = 0 + n_false_positives = 0 + + for expected_value, predicted_value in zip(expected_values, predicted_values, strict=True): + if predicted_value == positive_class: + if expected_value == positive_class: + n_true_positives += 1 + else: + n_false_positives += 1 + elif expected_value == positive_class: + n_false_negatives += 1 + + if (2 * n_true_positives + n_false_positives + n_false_negatives) == 0: + return 1.0 + return 2 * n_true_positives / (2 * n_true_positives + n_false_positives + n_false_negatives) diff --git a/src/safeds/ml/classification/_decision_tree.py b/src/safeds/ml/classical/classification/_decision_tree.py similarity index 73% rename from src/safeds/ml/classification/_decision_tree.py rename to src/safeds/ml/classical/classification/_decision_tree.py index ab3228940..5b183e8b4 100644 --- a/src/safeds/ml/classification/_decision_tree.py +++ b/src/safeds/ml/classical/classification/_decision_tree.py @@ -4,11 +4,13 @@ from sklearn.tree import DecisionTreeClassifier as sk_DecisionTreeClassifier -from safeds.ml._util_sklearn import fit, predict +from safeds.ml.classical._util_sklearn import fit, predict from ._classifier import Classifier if TYPE_CHECKING: + from sklearn.base import ClassifierMixin + from safeds.data.tabular.containers import Table, TaggedTable @@ -16,6 +18,7 @@ class DecisionTree(Classifier): """Decision tree classification.""" def __init__(self) -> None: + # Internal state self._wrapped_classifier: sk_DecisionTreeClassifier | None = None self._feature_names: list[str] | None = None self._target_name: str | None = None @@ -40,13 +43,21 @@ def fit(self, training_set: TaggedTable) -> DecisionTree: ------ LearningError If the training data contains invalid values or if the training failed. + UntaggedTableError + If the table is untagged. + NonNumericColumnError + If the training data contains non-numerical values. + MissingValuesColumnError + If the training data contains missing values. + DatasetMissesDataError + If the training data contains no rows. """ - wrapped_classifier = sk_DecisionTreeClassifier() + wrapped_classifier = self._get_sklearn_classifier() fit(wrapped_classifier, training_set) result = DecisionTree() result._wrapped_classifier = wrapped_classifier - result._feature_names = training_set.features.get_column_names() + result._feature_names = training_set.features.column_names result._target_name = training_set.target.name return result @@ -75,6 +86,12 @@ def predict(self, dataset: Table) -> TaggedTable: If the dataset misses feature columns. PredictionError If predicting with the given dataset failed. + NonNumericColumnError + If the dataset contains non-numerical values. + MissingValuesColumnError + If the dataset contains missing values. + DatasetMissesDataError + If the dataset contains no rows. """ return predict(self._wrapped_classifier, dataset, self._feature_names, self._target_name) @@ -88,3 +105,6 @@ def is_fitted(self) -> bool: Whether the classifier is fitted. """ return self._wrapped_classifier is not None + + def _get_sklearn_classifier(self) -> ClassifierMixin: + return sk_DecisionTreeClassifier() diff --git a/src/safeds/ml/classical/classification/_gradient_boosting.py b/src/safeds/ml/classical/classification/_gradient_boosting.py new file mode 100644 index 000000000..23804b3ca --- /dev/null +++ b/src/safeds/ml/classical/classification/_gradient_boosting.py @@ -0,0 +1,168 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING + +from sklearn.ensemble import GradientBoostingClassifier as sk_GradientBoostingClassifier + +from safeds.ml.classical._util_sklearn import fit, predict + +from ._classifier import Classifier + +if TYPE_CHECKING: + from sklearn.base import ClassifierMixin + + from safeds.data.tabular.containers import Table, TaggedTable + + +class GradientBoosting(Classifier): + """ + Gradient boosting classification. + + Parameters + ---------- + number_of_trees: int + The number of boosting stages to perform. Gradient boosting is fairly robust to over-fitting so a large + number usually results in better performance. + learning_rate : float + The larger the value, the more the model is influenced by each additional tree. If the learning rate is too + low, the model might underfit. If the learning rate is too high, the model might overfit. + + Raises + ------ + ValueError + If `number_of_trees` is less than or equal to 0 or `learning_rate` is non-positive. + """ + + def __init__(self, *, number_of_trees: int = 100, learning_rate: float = 0.1) -> None: + # Validation + if number_of_trees <= 0: + raise ValueError("The parameter 'number_of_trees' has to be greater than 0.") + if learning_rate <= 0: + raise ValueError("The parameter 'learning_rate' has to be greater than 0.") + + # Hyperparameters + self._number_of_trees = number_of_trees + self._learning_rate = learning_rate + + # Internal state + self._wrapped_classifier: sk_GradientBoostingClassifier | None = None + self._feature_names: list[str] | None = None + self._target_name: str | None = None + + @property + def number_of_trees(self) -> int: + """ + Get the number of trees (estimators) in the ensemble. + + Returns + ------- + result: int + The number of trees. + """ + return self._number_of_trees + + @property + def learning_rate(self) -> float: + """ + Get the learning rate. + + Returns + ------- + result: float + The learning rate. + """ + return self._learning_rate + + def fit(self, training_set: TaggedTable) -> GradientBoosting: + """ + Create a copy of this classifier and fit it with the given training data. + + This classifier is not modified. + + Parameters + ---------- + training_set : TaggedTable + The training data containing the feature and target vectors. + + Returns + ------- + fitted_classifier : GradientBoosting + The fitted classifier. + + Raises + ------ + LearningError + If the training data contains invalid values or if the training failed. + UntaggedTableError + If the table is untagged. + NonNumericColumnError + If the training data contains non-numerical values. + MissingValuesColumnError + If the training data contains missing values. + DatasetMissesDataError + If the training data contains no rows. + """ + wrapped_classifier = self._get_sklearn_classifier() + fit(wrapped_classifier, training_set) + + result = GradientBoosting(number_of_trees=self._number_of_trees, learning_rate=self._learning_rate) + result._wrapped_classifier = wrapped_classifier + result._feature_names = training_set.features.column_names + result._target_name = training_set.target.name + + return result + + def predict(self, dataset: Table) -> TaggedTable: + """ + Predict a target vector using a dataset containing feature vectors. The model has to be trained first. + + Parameters + ---------- + dataset : Table + The dataset containing the feature vectors. + + Returns + ------- + table : TaggedTable + A dataset containing the given feature vectors and the predicted target vector. + + Raises + ------ + ModelNotFittedError + If the model has not been fitted yet. + DatasetContainsTargetError + If the dataset contains the target column already. + DatasetMissesFeaturesError + If the dataset misses feature columns. + PredictionError + If predicting with the given dataset failed. + NonNumericColumnError + If the dataset contains non-numerical values. + MissingValuesColumnError + If the dataset contains missing values. + DatasetMissesDataError + If the dataset contains no rows. + """ + return predict(self._wrapped_classifier, dataset, self._feature_names, self._target_name) + + def is_fitted(self) -> bool: + """ + Check if the classifier is fitted. + + Returns + ------- + is_fitted : bool + Whether the classifier is fitted. + """ + return self._wrapped_classifier is not None + + def _get_sklearn_classifier(self) -> ClassifierMixin: + """ + Return a new wrapped Classifier from sklearn. + + Returns + ------- + wrapped_classifier: ClassifierMixin + The sklearn Classifier. + """ + return sk_GradientBoostingClassifier(n_estimators=self._number_of_trees, learning_rate=self._learning_rate) diff --git a/src/safeds/ml/classification/_k_nearest_neighbors.py b/src/safeds/ml/classical/classification/_k_nearest_neighbors.py similarity index 52% rename from src/safeds/ml/classification/_k_nearest_neighbors.py rename to src/safeds/ml/classical/classification/_k_nearest_neighbors.py index fe23bb5f9..d7ff20362 100644 --- a/src/safeds/ml/classification/_k_nearest_neighbors.py +++ b/src/safeds/ml/classical/classification/_k_nearest_neighbors.py @@ -4,11 +4,14 @@ from sklearn.neighbors import KNeighborsClassifier as sk_KNeighborsClassifier -from safeds.ml._util_sklearn import fit, predict +from safeds.exceptions import DatasetMissesDataError +from safeds.ml.classical._util_sklearn import fit, predict from ._classifier import Classifier if TYPE_CHECKING: + from sklearn.base import ClassifierMixin + from safeds.data.tabular.containers import Table, TaggedTable @@ -19,16 +22,40 @@ class KNearestNeighbors(Classifier): Parameters ---------- number_of_neighbors : int - The number of neighbors to be interpolated with. Has to be less than or equal to the sample size. + The number of neighbors to use for interpolation. Has to be greater than 0 (validated in the constructor) and + less than or equal to the sample size (validated when calling `fit`). + + Raises + ------ + ValueError + If `number_of_neighbors` is less than or equal to 0. """ def __init__(self, number_of_neighbors: int) -> None: + # Validation + if number_of_neighbors <= 0: + raise ValueError("The parameter 'number_of_neighbors' has to be greater than 0.") + + # Hyperparameters self._number_of_neighbors = number_of_neighbors + # Internal state self._wrapped_classifier: sk_KNeighborsClassifier | None = None self._feature_names: list[str] | None = None self._target_name: str | None = None + @property + def number_of_neighbors(self) -> int: + """ + Get the number of neighbors used for interpolation. + + Returns + ------- + result: int + The number of neighbors. + """ + return self._number_of_neighbors + def fit(self, training_set: TaggedTable) -> KNearestNeighbors: """ Create a copy of this classifier and fit it with the given training data. @@ -47,15 +74,34 @@ def fit(self, training_set: TaggedTable) -> KNearestNeighbors: Raises ------ + ValueError + If `number_of_neighbors` is greater than the sample size. LearningError If the training data contains invalid values or if the training failed. + UntaggedTableError + If the table is untagged. + NonNumericColumnError + If the training data contains non-numerical values. + MissingValuesColumnError + If the training data contains missing values. + DatasetMissesDataError + If the training data contains no rows. """ - wrapped_classifier = sk_KNeighborsClassifier(self._number_of_neighbors, n_jobs=-1) + if training_set.number_of_rows == 0: + raise DatasetMissesDataError + if self._number_of_neighbors > training_set.number_of_rows: + raise ValueError( + ( + f"The parameter 'number_of_neighbors' ({self._number_of_neighbors}) has to be less than or equal to" + f" the sample size ({training_set.number_of_rows})." + ), + ) + wrapped_classifier = self._get_sklearn_classifier() fit(wrapped_classifier, training_set) result = KNearestNeighbors(self._number_of_neighbors) result._wrapped_classifier = wrapped_classifier - result._feature_names = training_set.features.get_column_names() + result._feature_names = training_set.features.column_names result._target_name = training_set.target.name return result @@ -84,6 +130,12 @@ def predict(self, dataset: Table) -> TaggedTable: If the dataset misses feature columns. PredictionError If predicting with the given dataset failed. + NonNumericColumnError + If the dataset contains non-numerical values. + MissingValuesColumnError + If the dataset contains missing values. + DatasetMissesDataError + If the dataset contains no rows. """ return predict(self._wrapped_classifier, dataset, self._feature_names, self._target_name) @@ -97,3 +149,14 @@ def is_fitted(self) -> bool: Whether the classifier is fitted. """ return self._wrapped_classifier is not None + + def _get_sklearn_classifier(self) -> ClassifierMixin: + """ + Return a new wrapped Classifier from sklearn. + + Returns + ------- + wrapped_classifier: ClassifierMixin + The sklearn Classifier. + """ + return sk_KNeighborsClassifier(self._number_of_neighbors, n_jobs=-1) diff --git a/src/safeds/ml/classification/_logistic_regression.py b/src/safeds/ml/classical/classification/_logistic_regression.py similarity index 69% rename from src/safeds/ml/classification/_logistic_regression.py rename to src/safeds/ml/classical/classification/_logistic_regression.py index 46ed52786..b2a5cbeba 100644 --- a/src/safeds/ml/classification/_logistic_regression.py +++ b/src/safeds/ml/classical/classification/_logistic_regression.py @@ -4,11 +4,13 @@ from sklearn.linear_model import LogisticRegression as sk_LogisticRegression -from safeds.ml._util_sklearn import fit, predict +from safeds.ml.classical._util_sklearn import fit, predict from ._classifier import Classifier if TYPE_CHECKING: + from sklearn.base import ClassifierMixin + from safeds.data.tabular.containers import Table, TaggedTable @@ -16,6 +18,7 @@ class LogisticRegression(Classifier): """Regularized logistic regression.""" def __init__(self) -> None: + # Internal state self._wrapped_classifier: sk_LogisticRegression | None = None self._feature_names: list[str] | None = None self._target_name: str | None = None @@ -40,13 +43,21 @@ def fit(self, training_set: TaggedTable) -> LogisticRegression: ------ LearningError If the training data contains invalid values or if the training failed. + UntaggedTableError + If the table is untagged. + NonNumericColumnError + If the training data contains non-numerical values. + MissingValuesColumnError + If the training data contains missing values. + DatasetMissesDataError + If the training data contains no rows. """ - wrapped_classifier = sk_LogisticRegression(n_jobs=-1) + wrapped_classifier = self._get_sklearn_classifier() fit(wrapped_classifier, training_set) result = LogisticRegression() result._wrapped_classifier = wrapped_classifier - result._feature_names = training_set.features.get_column_names() + result._feature_names = training_set.features.column_names result._target_name = training_set.target.name return result @@ -75,6 +86,12 @@ def predict(self, dataset: Table) -> TaggedTable: If the dataset misses feature columns. PredictionError If predicting with the given dataset failed. + NonNumericColumnError + If the dataset contains non-numerical values. + MissingValuesColumnError + If the dataset contains missing values. + DatasetMissesDataError + If the dataset contains no rows. """ return predict(self._wrapped_classifier, dataset, self._feature_names, self._target_name) @@ -88,3 +105,14 @@ def is_fitted(self) -> bool: Whether the classifier is fitted. """ return self._wrapped_classifier is not None + + def _get_sklearn_classifier(self) -> ClassifierMixin: + """ + Return a new wrapped Classifier from sklearn. + + Returns + ------- + wrapped_classifier: ClassifierMixin + The sklearn Classifier. + """ + return sk_LogisticRegression(n_jobs=-1) diff --git a/src/safeds/ml/classification/_random_forest.py b/src/safeds/ml/classical/classification/_random_forest.py similarity index 54% rename from src/safeds/ml/classification/_random_forest.py rename to src/safeds/ml/classical/classification/_random_forest.py index 7ce3e7bec..850c5296b 100644 --- a/src/safeds/ml/classification/_random_forest.py +++ b/src/safeds/ml/classical/classification/_random_forest.py @@ -4,22 +4,55 @@ from sklearn.ensemble import RandomForestClassifier as sk_RandomForestClassifier -from safeds.ml._util_sklearn import fit, predict +from safeds.ml.classical._util_sklearn import fit, predict from ._classifier import Classifier if TYPE_CHECKING: + from sklearn.base import ClassifierMixin + from safeds.data.tabular.containers import Table, TaggedTable class RandomForest(Classifier): - """Random forest classification.""" + """Random forest classification. + + Parameters + ---------- + number_of_trees : int + The number of trees to be used in the random forest. Has to be greater than 0. + + Raises + ------ + ValueError + If `number_of_trees` is less than or equal to 0. + """ + + def __init__(self, *, number_of_trees: int = 100) -> None: + # Validation + if number_of_trees < 1: + raise ValueError("The parameter 'number_of_trees' has to be greater than 0.") + + # Hyperparameters + self._number_of_trees = number_of_trees - def __init__(self) -> None: + # Internal state self._wrapped_classifier: sk_RandomForestClassifier | None = None self._feature_names: list[str] | None = None self._target_name: str | None = None + @property + def number_of_trees(self) -> int: + """ + Get the number of trees used in the random forest. + + Returns + ------- + result: int + The number of trees. + """ + return self._number_of_trees + def fit(self, training_set: TaggedTable) -> RandomForest: """ Create a copy of this classifier and fit it with the given training data. @@ -40,13 +73,21 @@ def fit(self, training_set: TaggedTable) -> RandomForest: ------ LearningError If the training data contains invalid values or if the training failed. + UntaggedTableError + If the table is untagged. + NonNumericColumnError + If the training data contains non-numerical values. + MissingValuesColumnError + If the training data contains missing values. + DatasetMissesDataError + If the training data contains no rows. """ - wrapped_classifier = sk_RandomForestClassifier(n_jobs=-1) + wrapped_classifier = self._get_sklearn_classifier() fit(wrapped_classifier, training_set) - result = RandomForest() + result = RandomForest(number_of_trees=self._number_of_trees) result._wrapped_classifier = wrapped_classifier - result._feature_names = training_set.features.get_column_names() + result._feature_names = training_set.features.column_names result._target_name = training_set.target.name return result @@ -75,6 +116,12 @@ def predict(self, dataset: Table) -> TaggedTable: If the dataset misses feature columns. PredictionError If predicting with the given dataset failed. + NonNumericColumnError + If the dataset contains non-numerical values. + MissingValuesColumnError + If the dataset contains missing values. + DatasetMissesDataError + If the dataset contains no rows. """ return predict(self._wrapped_classifier, dataset, self._feature_names, self._target_name) @@ -88,3 +135,14 @@ def is_fitted(self) -> bool: Whether the classifier is fitted. """ return self._wrapped_classifier is not None + + def _get_sklearn_classifier(self) -> ClassifierMixin: + """ + Return a new wrapped Classifier from sklearn. + + Returns + ------- + wrapped_classifier: ClassifierMixin + The sklearn Classifier. + """ + return sk_RandomForestClassifier(self._number_of_trees, n_jobs=-1) diff --git a/src/safeds/ml/classical/classification/_support_vector_machine.py b/src/safeds/ml/classical/classification/_support_vector_machine.py new file mode 100644 index 000000000..57a528d9f --- /dev/null +++ b/src/safeds/ml/classical/classification/_support_vector_machine.py @@ -0,0 +1,255 @@ +from __future__ import annotations + +from abc import ABC, abstractmethod +from typing import TYPE_CHECKING + +from sklearn.svm import SVC as sk_SVC # noqa: N811 + +from safeds.ml.classical._util_sklearn import fit, predict +from safeds.ml.classical.classification import Classifier + +if TYPE_CHECKING: + from sklearn.base import ClassifierMixin + + from safeds.data.tabular.containers import Table, TaggedTable + + +class SupportVectorMachineKernel(ABC): + """The abstract base class of the different subclasses supported by the `Kernel`.""" + + @abstractmethod + def get_sklearn_kernel(self) -> object: + """ + Get the kernel of the given SupportVectorMachine. + + Returns + ------- + object + The kernel of the SupportVectorMachine. + """ + + +class SupportVectorMachine(Classifier): + """ + Support vector machine. + + Parameters + ---------- + c: float + The strength of regularization. Must be strictly positive. + kernel: The type of kernel to be used. Defaults to None. + + Raises + ------ + ValueError + If `c` is less than or equal to 0. + """ + + def __init__(self, *, c: float = 1.0, kernel: SupportVectorMachineKernel | None = None) -> None: + # Internal state + self._wrapped_classifier: sk_SVC | None = None + self._feature_names: list[str] | None = None + self._target_name: str | None = None + + # Hyperparameters + if c <= 0: + raise ValueError("The parameter 'c' has to be strictly positive.") + self._c = c + self._kernel = kernel + + @property + def c(self) -> float: + """ + Get the regularization strength. + + Returns + ------- + result: float + The regularization strength. + """ + return self._c + + @property + def kernel(self) -> SupportVectorMachineKernel | None: + """ + Get the type of kernel used. + + Returns + ------- + result: SupportVectorMachineKernel | None + The type of kernel used. + """ + return self._kernel + + class Kernel: + class Linear(SupportVectorMachineKernel): + def get_sklearn_kernel(self) -> str: + """ + Get the name of the linear kernel. + + Returns + ------- + result: str + The name of the linear kernel. + """ + return "linear" + + class Polynomial(SupportVectorMachineKernel): + def __init__(self, degree: int): + if degree < 1: + raise ValueError("The parameter 'degree' has to be greater than or equal to 1.") + self._degree = degree + + def get_sklearn_kernel(self) -> str: + """ + Get the name of the polynomial kernel. + + Returns + ------- + result: str + The name of the polynomial kernel. + """ + return "poly" + + class Sigmoid(SupportVectorMachineKernel): + def get_sklearn_kernel(self) -> str: + """ + Get the name of the sigmoid kernel. + + Returns + ------- + result: str + The name of the sigmoid kernel. + """ + return "sigmoid" + + class RadialBasisFunction(SupportVectorMachineKernel): + def get_sklearn_kernel(self) -> str: + """ + Get the name of the radial basis function (RBF) kernel. + + Returns + ------- + result: str + The name of the RBF kernel. + """ + return "rbf" + + def _get_kernel_name(self) -> str: + """ + Get the name of the kernel. + + Returns + ------- + result: str + The name of the kernel. + + Raises + ------ + TypeError + If the kernel type is invalid. + """ + if isinstance(self.kernel, SupportVectorMachine.Kernel.Linear): + return "linear" + elif isinstance(self.kernel, SupportVectorMachine.Kernel.Polynomial): + return "poly" + elif isinstance(self.kernel, SupportVectorMachine.Kernel.Sigmoid): + return "sigmoid" + elif isinstance(self.kernel, SupportVectorMachine.Kernel.RadialBasisFunction): + return "rbf" + else: + raise TypeError("Invalid kernel type.") + + def fit(self, training_set: TaggedTable) -> SupportVectorMachine: + """ + Create a copy of this classifier and fit it with the given training data. + + This classifier is not modified. + + Parameters + ---------- + training_set : TaggedTable + The training data containing the feature and target vectors. + + Returns + ------- + fitted_classifier : SupportVectorMachine + The fitted classifier. + + Raises + ------ + LearningError + If the training data contains invalid values or if the training failed. + UntaggedTableError + If the table is untagged. + NonNumericColumnError + If the training data contains non-numerical values. + MissingValuesColumnError + If the training data contains missing values. + DatasetMissesDataError + If the training data contains no rows. + """ + wrapped_classifier = self._get_sklearn_classifier() + fit(wrapped_classifier, training_set) + + result = SupportVectorMachine(c=self._c, kernel=self._kernel) + result._wrapped_classifier = wrapped_classifier + result._feature_names = training_set.features.column_names + result._target_name = training_set.target.name + + return result + + def predict(self, dataset: Table) -> TaggedTable: + """ + Predict a target vector using a dataset containing feature vectors. The model has to be trained first. + + Parameters + ---------- + dataset : Table + The dataset containing the feature vectors. + + Returns + ------- + table : TaggedTable + A dataset containing the given feature vectors and the predicted target vector. + + Raises + ------ + ModelNotFittedError + If the model has not been fitted yet. + DatasetContainsTargetError + If the dataset contains the target column already. + DatasetMissesFeaturesError + If the dataset misses feature columns. + PredictionError + If predicting with the given dataset failed. + NonNumericColumnError + If the dataset contains non-numerical values. + MissingValuesColumnError + If the dataset contains missing values. + DatasetMissesDataError + If the dataset contains no rows. + """ + return predict(self._wrapped_classifier, dataset, self._feature_names, self._target_name) + + def is_fitted(self) -> bool: + """ + Check if the classifier is fitted. + + Returns + ------- + is_fitted : bool + Whether the classifier is fitted. + """ + return self._wrapped_classifier is not None + + def _get_sklearn_classifier(self) -> ClassifierMixin: + """ + Return a new wrapped Classifier from sklearn. + + Returns + ------- + wrapped_classifier: ClassifierMixin + The sklearn Classifier. + """ + return sk_SVC(C=self._c) diff --git a/src/safeds/ml/regression/__init__.py b/src/safeds/ml/classical/regression/__init__.py similarity index 83% rename from src/safeds/ml/regression/__init__.py rename to src/safeds/ml/classical/regression/__init__.py index 7e4aceffd..9d45d16e6 100644 --- a/src/safeds/ml/regression/__init__.py +++ b/src/safeds/ml/classical/regression/__init__.py @@ -3,13 +3,14 @@ from ._ada_boost import AdaBoost from ._decision_tree import DecisionTree from ._elastic_net_regression import ElasticNetRegression -from ._gradient_boosting_regression import GradientBoosting +from ._gradient_boosting import GradientBoosting from ._k_nearest_neighbors import KNearestNeighbors from ._lasso_regression import LassoRegression from ._linear_regression import LinearRegression from ._random_forest import RandomForest from ._regressor import Regressor from ._ridge_regression import RidgeRegression +from ._support_vector_machine import SupportVectorMachine __all__ = [ "AdaBoost", @@ -22,4 +23,5 @@ "RandomForest", "Regressor", "RidgeRegression", + "SupportVectorMachine", ] diff --git a/src/safeds/ml/classical/regression/_ada_boost.py b/src/safeds/ml/classical/regression/_ada_boost.py new file mode 100644 index 000000000..d4d39bf85 --- /dev/null +++ b/src/safeds/ml/classical/regression/_ada_boost.py @@ -0,0 +1,198 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING + +from sklearn.ensemble import AdaBoostRegressor as sk_AdaBoostRegressor + +from safeds.ml.classical._util_sklearn import fit, predict + +from ._regressor import Regressor + +if TYPE_CHECKING: + from sklearn.base import RegressorMixin + + from safeds.data.tabular.containers import Table, TaggedTable + + +class AdaBoost(Regressor): + """ + Ada Boost regression. + + Parameters + ---------- + learner: Classifier + The learner from which the boosted ensemble is built. + maximum_number_of_learners: int + The maximum number of learners at which boosting is terminated. In case of perfect fit, the learning procedure + is stopped early. Has to be greater than 0. + learning_rate : float + Weight applied to each regressor at each boosting iteration. A higher learning rate increases the contribution + of each regressor. Has to be greater than 0. + + Raises + ------ + ValueError + If `maximum_number_of_learners` or `learning_rate` are less than or equal to 0 + """ + + def __init__( + self, + *, + learner: Regressor | None = None, + maximum_number_of_learners: int = 50, + learning_rate: float = 1.0, + ) -> None: + # Validation + if maximum_number_of_learners <= 0: + raise ValueError("The parameter 'maximum_number_of_learners' has to be grater than 0.") + if learning_rate <= 0: + raise ValueError("The parameter 'learning_rate' has to be greater than 0.") + + # Hyperparameters + self._learner = learner + self._maximum_number_of_learners = maximum_number_of_learners + self._learning_rate = learning_rate + + # Internal state + self._wrapped_regressor: sk_AdaBoostRegressor | None = None + self._feature_names: list[str] | None = None + self._target_name: str | None = None + + @property + def learner(self) -> Regressor | None: + """ + Get the base learner used for training the ensemble. + + Returns + ------- + result: Classifier | None + The base learner. + """ + return self._learner + + @property + def maximum_number_of_learners(self) -> int: + """ + Get the maximum number of learners in the ensemble. + + Returns + ------- + result: int + The maximum number of learners. + """ + return self._maximum_number_of_learners + + @property + def learning_rate(self) -> float: + """ + Get the learning rate. + + Returns + ------- + result: float + The learning rate. + """ + return self._learning_rate + + def fit(self, training_set: TaggedTable) -> AdaBoost: + """ + Create a copy of this regressor and fit it with the given training data. + + This regressor is not modified. + + Parameters + ---------- + training_set : TaggedTable + The training data containing the feature and target vectors. + + Returns + ------- + fitted_regressor : AdaBoost + The fitted regressor. + + Raises + ------ + LearningError + If the training data contains invalid values or if the training failed. + UntaggedTableError + If the table is untagged. + NonNumericColumnError + If the training data contains non-numerical values. + MissingValuesColumnError + If the training data contains missing values. + DatasetMissesDataError + If the training data contains no rows. + """ + wrapped_regressor = self._get_sklearn_regressor() + fit(wrapped_regressor, training_set) + + result = AdaBoost( + learner=self._learner, + maximum_number_of_learners=self._maximum_number_of_learners, + learning_rate=self._learning_rate, + ) + result._wrapped_regressor = wrapped_regressor + result._feature_names = training_set.features.column_names + result._target_name = training_set.target.name + + return result + + def predict(self, dataset: Table) -> TaggedTable: + """ + Predict a target vector using a dataset containing feature vectors. The model has to be trained first. + + Parameters + ---------- + dataset : Table + The dataset containing the feature vectors. + + Returns + ------- + table : TaggedTable + A dataset containing the given feature vectors and the predicted target vector. + + Raises + ------ + ModelNotFittedError + If the model has not been fitted yet. + DatasetContainsTargetError + If the dataset contains the target column already. + DatasetMissesFeaturesError + If the dataset misses feature columns. + PredictionError + If predicting with the given dataset failed. + NonNumericColumnError + If the dataset contains non-numerical values. + MissingValuesColumnError + If the dataset contains missing values. + DatasetMissesDataError + If the dataset contains no rows. + """ + return predict(self._wrapped_regressor, dataset, self._feature_names, self._target_name) + + def is_fitted(self) -> bool: + """ + Check if the regressor is fitted. + + Returns + ------- + is_fitted : bool + Whether the regressor is fitted. + """ + return self._wrapped_regressor is not None + + def _get_sklearn_regressor(self) -> RegressorMixin: + """ + Return a new wrapped Regressor from sklearn. + + Returns + ------- + wrapped_regressor: RegressorMixin + The sklearn Regressor. + """ + learner = self._learner._get_sklearn_regressor() if self._learner is not None else None + return sk_AdaBoostRegressor( + estimator=learner, + n_estimators=self._maximum_number_of_learners, + learning_rate=self._learning_rate, + ) diff --git a/src/safeds/ml/regression/_decision_tree.py b/src/safeds/ml/classical/regression/_decision_tree.py similarity index 69% rename from src/safeds/ml/regression/_decision_tree.py rename to src/safeds/ml/classical/regression/_decision_tree.py index 1d02839d4..91beb47fb 100644 --- a/src/safeds/ml/regression/_decision_tree.py +++ b/src/safeds/ml/classical/regression/_decision_tree.py @@ -4,11 +4,13 @@ from sklearn.tree import DecisionTreeRegressor as sk_DecisionTreeRegressor -from safeds.ml._util_sklearn import fit, predict +from safeds.ml.classical._util_sklearn import fit, predict from ._regressor import Regressor if TYPE_CHECKING: + from sklearn.base import RegressorMixin + from safeds.data.tabular.containers import Table, TaggedTable @@ -16,6 +18,7 @@ class DecisionTree(Regressor): """Decision tree regression.""" def __init__(self) -> None: + # Internal state self._wrapped_regressor: sk_DecisionTreeRegressor | None = None self._feature_names: list[str] | None = None self._target_name: str | None = None @@ -40,13 +43,21 @@ def fit(self, training_set: TaggedTable) -> DecisionTree: ------ LearningError If the training data contains invalid values or if the training failed. + UntaggedTableError + If the table is untagged. + NonNumericColumnError + If the training data contains non-numerical values. + MissingValuesColumnError + If the training data contains missing values. + DatasetMissesDataError + If the training data contains no rows. """ - wrapped_regressor = sk_DecisionTreeRegressor() + wrapped_regressor = self._get_sklearn_regressor() fit(wrapped_regressor, training_set) result = DecisionTree() result._wrapped_regressor = wrapped_regressor - result._feature_names = training_set.features.get_column_names() + result._feature_names = training_set.features.column_names result._target_name = training_set.target.name return result @@ -75,6 +86,12 @@ def predict(self, dataset: Table) -> TaggedTable: If the dataset misses feature columns. PredictionError If predicting with the given dataset failed. + NonNumericColumnError + If the dataset contains non-numerical values. + MissingValuesColumnError + If the dataset contains missing values. + DatasetMissesDataError + If the dataset contains no rows. """ return predict(self._wrapped_regressor, dataset, self._feature_names, self._target_name) @@ -88,3 +105,14 @@ def is_fitted(self) -> bool: Whether the regressor is fitted. """ return self._wrapped_regressor is not None + + def _get_sklearn_regressor(self) -> RegressorMixin: + """ + Return a new wrapped Regressor from sklearn. + + Returns + ------- + wrapped_regressor: RegressorMixin + The sklearn Regressor. + """ + return sk_DecisionTreeRegressor() diff --git a/src/safeds/ml/classical/regression/_elastic_net_regression.py b/src/safeds/ml/classical/regression/_elastic_net_regression.py new file mode 100644 index 000000000..054bcb3cf --- /dev/null +++ b/src/safeds/ml/classical/regression/_elastic_net_regression.py @@ -0,0 +1,193 @@ +from __future__ import annotations + +import warnings +from typing import TYPE_CHECKING +from warnings import warn + +from sklearn.linear_model import ElasticNet as sk_ElasticNet + +from safeds.ml.classical._util_sklearn import fit, predict + +from ._regressor import Regressor + +if TYPE_CHECKING: + from sklearn.base import RegressorMixin + + from safeds.data.tabular.containers import Table, TaggedTable + + +class ElasticNetRegression(Regressor): + """Elastic net regression. + + Parameters + ---------- + alpha : float + Controls the regularization of the model. The higher the value, the more regularized it becomes. + lasso_ratio: float + Number between 0 and 1 that controls the ratio between Lasso and Ridge regularization. If 0, only Ridge + regularization is used. If 1, only Lasso regularization is used. + + Raises + ------ + ValueError + If `alpha` is negative or `lasso_ratio` is not between 0 and 1. + """ + + def __init__(self, *, alpha: float = 1.0, lasso_ratio: float = 0.5) -> None: + # Validation + if alpha < 0: + raise ValueError("The parameter 'alpha' must be non-negative") + if alpha == 0: + warn( + ( + "Setting alpha to zero makes this model equivalent to LinearRegression. You should use " + "LinearRegression instead for better numerical stability." + ), + UserWarning, + stacklevel=2, + ) + if lasso_ratio < 0 or lasso_ratio > 1: + raise ValueError("The parameter 'lasso_ratio' must be between 0 and 1.") + elif lasso_ratio == 0: + warnings.warn( + ( + "ElasticNetRegression with lasso_ratio = 0 is essentially RidgeRegression." + " Use RidgeRegression instead for better numerical stability." + ), + stacklevel=2, + ) + elif lasso_ratio == 1: + warnings.warn( + ( + "ElasticNetRegression with lasso_ratio = 0 is essentially LassoRegression." + " Use LassoRegression instead for better numerical stability." + ), + stacklevel=2, + ) + + # Hyperparameters + self._alpha = alpha + self._lasso_ratio = lasso_ratio + + # Internal state + self._wrapped_regressor: sk_ElasticNet | None = None + self._feature_names: list[str] | None = None + self._target_name: str | None = None + + @property + def alpha(self) -> float: + """ + Get the regularization of the model. + + Returns + ------- + result: float + The regularization of the model. + """ + return self._alpha + + @property + def lasso_ratio(self) -> float: + """ + Get the ratio between Lasso and Ridge regularization. + + Returns + ------- + result: float + The ratio between Lasso and Ridge regularization. + """ + return self._lasso_ratio + + def fit(self, training_set: TaggedTable) -> ElasticNetRegression: + """ + Create a copy of this regressor and fit it with the given training data. + + This regressor is not modified. + + Parameters + ---------- + training_set : TaggedTable + The training data containing the feature and target vectors. + + Returns + ------- + fitted_regressor : ElasticNetRegression + The fitted regressor. + + Raises + ------ + LearningError + If the training data contains invalid values or if the training failed. + UntaggedTableError + If the table is untagged. + NonNumericColumnError + If the training data contains non-numerical values. + MissingValuesColumnError + If the training data contains missing values. + DatasetMissesDataError + If the training data contains no rows. + """ + wrapped_regressor = self._get_sklearn_regressor() + fit(wrapped_regressor, training_set) + + result = ElasticNetRegression(alpha=self._alpha, lasso_ratio=self._lasso_ratio) + result._wrapped_regressor = wrapped_regressor + result._feature_names = training_set.features.column_names + result._target_name = training_set.target.name + + return result + + def predict(self, dataset: Table) -> TaggedTable: + """ + Predict a target vector using a dataset containing feature vectors. The model has to be trained first. + + Parameters + ---------- + dataset : Table + The dataset containing the feature vectors. + + Returns + ------- + table : TaggedTable + A dataset containing the given feature vectors and the predicted target vector. + + Raises + ------ + ModelNotFittedError + If the model has not been fitted yet. + DatasetContainsTargetError + If the dataset contains the target column already. + DatasetMissesFeaturesError + If the dataset misses feature columns. + PredictionError + If predicting with the given dataset failed. + NonNumericColumnError + If the dataset contains non-numerical values. + MissingValuesColumnError + If the dataset contains missing values. + DatasetMissesDataError + If the dataset contains no rows. + """ + return predict(self._wrapped_regressor, dataset, self._feature_names, self._target_name) + + def is_fitted(self) -> bool: + """ + Check if the regressor is fitted. + + Returns + ------- + is_fitted : bool + Whether the regressor is fitted. + """ + return self._wrapped_regressor is not None + + def _get_sklearn_regressor(self) -> RegressorMixin: + """ + Return a new wrapped Regressor from sklearn. + + Returns + ------- + wrapped_regressor: RegressorMixin + The sklearn Regressor. + """ + return sk_ElasticNet(alpha=self._alpha, l1_ratio=self._lasso_ratio) diff --git a/src/safeds/ml/classical/regression/_gradient_boosting.py b/src/safeds/ml/classical/regression/_gradient_boosting.py new file mode 100644 index 000000000..b783f4511 --- /dev/null +++ b/src/safeds/ml/classical/regression/_gradient_boosting.py @@ -0,0 +1,168 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING + +from sklearn.ensemble import GradientBoostingRegressor as sk_GradientBoostingRegressor + +from safeds.ml.classical._util_sklearn import fit, predict + +from ._regressor import Regressor + +if TYPE_CHECKING: + from sklearn.base import RegressorMixin + + from safeds.data.tabular.containers import Table, TaggedTable + + +class GradientBoosting(Regressor): + """ + Gradient boosting regression. + + Parameters + ---------- + number_of_trees: int + The number of boosting stages to perform. Gradient boosting is fairly robust to over-fitting so a large + number usually results in better performance. + learning_rate : float + The larger the value, the more the model is influenced by each additional tree. If the learning rate is too + low, the model might underfit. If the learning rate is too high, the model might overfit. + + Raises + ------ + ValueError + If `number_of_trees` is less than or equal to 0 or `learning_rate` is non-positive. + """ + + def __init__(self, *, number_of_trees: int = 100, learning_rate: float = 0.1) -> None: + # Validation + if number_of_trees <= 0: + raise ValueError("The parameter 'number_of_trees' has to be greater than 0.") + if learning_rate <= 0: + raise ValueError("The parameter 'learning_rate' has to be greater than 0.") + + # Hyperparameters + self._number_of_trees = number_of_trees + self._learning_rate = learning_rate + + # Internal state + self._wrapped_regressor: sk_GradientBoostingRegressor | None = None + self._feature_names: list[str] | None = None + self._target_name: str | None = None + + @property + def number_of_trees(self) -> int: + """ + Get the number of trees (estimators) in the ensemble. + + Returns + ------- + result: int + The number of trees. + """ + return self._number_of_trees + + @property + def learning_rate(self) -> float: + """ + Get the learning rate. + + Returns + ------- + result: float + The learning rate. + """ + return self._learning_rate + + def fit(self, training_set: TaggedTable) -> GradientBoosting: + """ + Create a copy of this regressor and fit it with the given training data. + + This regressor is not modified. + + Parameters + ---------- + training_set : TaggedTable + The training data containing the feature and target vectors. + + Returns + ------- + fitted_regressor : GradientBoosting + The fitted regressor. + + Raises + ------ + LearningError + If the training data contains invalid values or if the training failed. + UntaggedTableError + If the table is untagged. + NonNumericColumnError + If the training data contains non-numerical values. + MissingValuesColumnError + If the training data contains missing values. + DatasetMissesDataError + If the training data contains no rows. + """ + wrapped_regressor = self._get_sklearn_regressor() + fit(wrapped_regressor, training_set) + + result = GradientBoosting(number_of_trees=self._number_of_trees, learning_rate=self._learning_rate) + result._wrapped_regressor = wrapped_regressor + result._feature_names = training_set.features.column_names + result._target_name = training_set.target.name + + return result + + def predict(self, dataset: Table) -> TaggedTable: + """ + Predict a target vector using a dataset containing feature vectors. The model has to be trained first. + + Parameters + ---------- + dataset : Table + The dataset containing the feature vectors. + + Returns + ------- + table : TaggedTable + A dataset containing the given feature vectors and the predicted target vector. + + Raises + ------ + ModelNotFittedError + If the model has not been fitted yet. + DatasetContainsTargetError + If the dataset contains the target column already. + DatasetMissesFeaturesError + If the dataset misses feature columns. + PredictionError + If predicting with the given dataset failed. + NonNumericColumnError + If the dataset contains non-numerical values. + MissingValuesColumnError + If the dataset contains missing values. + DatasetMissesDataError + If the dataset contains no rows. + """ + return predict(self._wrapped_regressor, dataset, self._feature_names, self._target_name) + + def is_fitted(self) -> bool: + """ + Check if the regressor is fitted. + + Returns + ------- + is_fitted : bool + Whether the regressor is fitted. + """ + return self._wrapped_regressor is not None + + def _get_sklearn_regressor(self) -> RegressorMixin: + """ + Return a new wrapped Regressor from sklearn. + + Returns + ------- + wrapped_regressor: RegressorMixin + The sklearn Regressor. + """ + return sk_GradientBoostingRegressor(n_estimators=self._number_of_trees, learning_rate=self._learning_rate) diff --git a/src/safeds/ml/regression/_k_nearest_neighbors.py b/src/safeds/ml/classical/regression/_k_nearest_neighbors.py similarity index 52% rename from src/safeds/ml/regression/_k_nearest_neighbors.py rename to src/safeds/ml/classical/regression/_k_nearest_neighbors.py index b5b527e2c..4da871342 100644 --- a/src/safeds/ml/regression/_k_nearest_neighbors.py +++ b/src/safeds/ml/classical/regression/_k_nearest_neighbors.py @@ -4,11 +4,14 @@ from sklearn.neighbors import KNeighborsRegressor as sk_KNeighborsRegressor -from safeds.ml._util_sklearn import fit, predict +from safeds.exceptions import DatasetMissesDataError +from safeds.ml.classical._util_sklearn import fit, predict from ._regressor import Regressor if TYPE_CHECKING: + from sklearn.base import RegressorMixin + from safeds.data.tabular.containers import Table, TaggedTable @@ -19,16 +22,40 @@ class KNearestNeighbors(Regressor): Parameters ---------- number_of_neighbors : int - The number of neighbors to be interpolated with. Has to be less than or equal than the sample size. + The number of neighbors to use for interpolation. Has to be greater than 0 (validated in the constructor) and + less than or equal to the sample size (validated when calling `fit`). + + Raises + ------ + ValueError + If `number_of_neighbors` is less than or equal to 0. """ def __init__(self, number_of_neighbors: int) -> None: + # Validation + if number_of_neighbors <= 0: + raise ValueError("The parameter 'number_of_neighbors' has to be greater than 0.") + + # Hyperparameters self._number_of_neighbors = number_of_neighbors + # Internal state self._wrapped_regressor: sk_KNeighborsRegressor | None = None self._feature_names: list[str] | None = None self._target_name: str | None = None + @property + def number_of_neighbors(self) -> int: + """ + Get the number of neighbors used for interpolation. + + Returns + ------- + result: int + The number of neighbors. + """ + return self._number_of_neighbors + def fit(self, training_set: TaggedTable) -> KNearestNeighbors: """ Create a copy of this regressor and fit it with the given training data. @@ -47,15 +74,35 @@ def fit(self, training_set: TaggedTable) -> KNearestNeighbors: Raises ------ + ValueError + If `number_of_neighbors` is greater than the sample size. LearningError If the training data contains invalid values or if the training failed. + UntaggedTableError + If the table is untagged. + NonNumericColumnError + If the training data contains non-numerical values. + MissingValuesColumnError + If the training data contains missing values. + DatasetMissesDataError + If the training data contains no rows. """ - wrapped_regressor = sk_KNeighborsRegressor(self._number_of_neighbors, n_jobs=-1) + if training_set.number_of_rows == 0: + raise DatasetMissesDataError + if self._number_of_neighbors > training_set.number_of_rows: + raise ValueError( + ( + f"The parameter 'number_of_neighbors' ({self._number_of_neighbors}) has to be less than or equal to" + f" the sample size ({training_set.number_of_rows})." + ), + ) + + wrapped_regressor = self._get_sklearn_regressor() fit(wrapped_regressor, training_set) result = KNearestNeighbors(self._number_of_neighbors) result._wrapped_regressor = wrapped_regressor - result._feature_names = training_set.features.get_column_names() + result._feature_names = training_set.features.column_names result._target_name = training_set.target.name return result @@ -84,6 +131,12 @@ def predict(self, dataset: Table) -> TaggedTable: If the dataset misses feature columns. PredictionError If predicting with the given dataset failed. + NonNumericColumnError + If the dataset contains non-numerical values. + MissingValuesColumnError + If the dataset contains missing values. + DatasetMissesDataError + If the dataset contains no rows. """ return predict(self._wrapped_regressor, dataset, self._feature_names, self._target_name) @@ -97,3 +150,14 @@ def is_fitted(self) -> bool: Whether the regressor is fitted. """ return self._wrapped_regressor is not None + + def _get_sklearn_regressor(self) -> RegressorMixin: + """ + Return a new wrapped Regressor from sklearn. + + Returns + ------- + wrapped_regressor: RegressorMixin + The sklearn Regressor. + """ + return sk_KNeighborsRegressor(self._number_of_neighbors, n_jobs=-1) diff --git a/src/safeds/ml/regression/_lasso_regression.py b/src/safeds/ml/classical/regression/_lasso_regression.py similarity index 51% rename from src/safeds/ml/regression/_lasso_regression.py rename to src/safeds/ml/classical/regression/_lasso_regression.py index f3ceaae90..ac238e330 100644 --- a/src/safeds/ml/regression/_lasso_regression.py +++ b/src/safeds/ml/classical/regression/_lasso_regression.py @@ -1,25 +1,68 @@ from __future__ import annotations from typing import TYPE_CHECKING +from warnings import warn from sklearn.linear_model import Lasso as sk_Lasso -from safeds.ml._util_sklearn import fit, predict +from safeds.ml.classical._util_sklearn import fit, predict from ._regressor import Regressor if TYPE_CHECKING: + from sklearn.base import RegressorMixin + from safeds.data.tabular.containers import Table, TaggedTable class LassoRegression(Regressor): - """Lasso regression.""" - - def __init__(self) -> None: + """Lasso regression. + + Parameters + ---------- + alpha : float + Controls the regularization of the model. The higher the value, the more regularized it becomes. + + Raises + ------ + ValueError + If `alpha` is negative. + """ + + def __init__(self, *, alpha: float = 1.0) -> None: + # Validation + if alpha < 0: + raise ValueError("The parameter 'alpha' must be non-negative") + if alpha == 0: + warn( + ( + "Setting alpha to zero makes this model equivalent to LinearRegression. You should use " + "LinearRegression instead for better numerical stability." + ), + UserWarning, + stacklevel=2, + ) + + # Hyperparameters + self._alpha = alpha + + # Internal state self._wrapped_regressor: sk_Lasso | None = None self._feature_names: list[str] | None = None self._target_name: str | None = None + @property + def alpha(self) -> float: + """ + Get the regularization of the model. + + Returns + ------- + result: float + The regularization of the model. + """ + return self._alpha + def fit(self, training_set: TaggedTable) -> LassoRegression: """ Create a copy of this regressor and fit it with the given training data. @@ -40,13 +83,21 @@ def fit(self, training_set: TaggedTable) -> LassoRegression: ------ LearningError If the training data contains invalid values or if the training failed. + UntaggedTableError + If the table is untagged. + NonNumericColumnError + If the training data contains non-numerical values. + MissingValuesColumnError + If the training data contains missing values. + DatasetMissesDataError + If the training data contains no rows. """ - wrapped_regressor = sk_Lasso() + wrapped_regressor = self._get_sklearn_regressor() fit(wrapped_regressor, training_set) - result = LassoRegression() + result = LassoRegression(alpha=self._alpha) result._wrapped_regressor = wrapped_regressor - result._feature_names = training_set.features.get_column_names() + result._feature_names = training_set.features.column_names result._target_name = training_set.target.name return result @@ -75,6 +126,12 @@ def predict(self, dataset: Table) -> TaggedTable: If the dataset misses feature columns. PredictionError If predicting with the given dataset failed. + NonNumericColumnError + If the dataset contains non-numerical values. + MissingValuesColumnError + If the dataset contains missing values. + DatasetMissesDataError + If the dataset contains no rows. """ return predict(self._wrapped_regressor, dataset, self._feature_names, self._target_name) @@ -88,3 +145,14 @@ def is_fitted(self) -> bool: Whether the regressor is fitted. """ return self._wrapped_regressor is not None + + def _get_sklearn_regressor(self) -> RegressorMixin: + """ + Return a new wrapped Regressor from sklearn. + + Returns + ------- + wrapped_regressor: RegressorMixin + The sklearn Regressor. + """ + return sk_Lasso(alpha=self._alpha) diff --git a/src/safeds/ml/regression/_linear_regression.py b/src/safeds/ml/classical/regression/_linear_regression.py similarity index 69% rename from src/safeds/ml/regression/_linear_regression.py rename to src/safeds/ml/classical/regression/_linear_regression.py index 14c0ef0ea..1c60a7114 100644 --- a/src/safeds/ml/regression/_linear_regression.py +++ b/src/safeds/ml/classical/regression/_linear_regression.py @@ -4,11 +4,13 @@ from sklearn.linear_model import LinearRegression as sk_LinearRegression -from safeds.ml._util_sklearn import fit, predict +from safeds.ml.classical._util_sklearn import fit, predict from ._regressor import Regressor if TYPE_CHECKING: + from sklearn.base import RegressorMixin + from safeds.data.tabular.containers import Table, TaggedTable @@ -16,6 +18,7 @@ class LinearRegression(Regressor): """Linear regression.""" def __init__(self) -> None: + # Internal state self._wrapped_regressor: sk_LinearRegression | None = None self._feature_names: list[str] | None = None self._target_name: str | None = None @@ -40,13 +43,21 @@ def fit(self, training_set: TaggedTable) -> LinearRegression: ------ LearningError If the training data contains invalid values or if the training failed. + UntaggedTableError + If the table is untagged. + NonNumericColumnError + If the training data contains non-numerical values. + MissingValuesColumnError + If the training data contains missing values. + DatasetMissesDataError + If the training data contains no rows. """ - wrapped_regressor = sk_LinearRegression(n_jobs=-1) + wrapped_regressor = self._get_sklearn_regressor() fit(wrapped_regressor, training_set) result = LinearRegression() result._wrapped_regressor = wrapped_regressor - result._feature_names = training_set.features.get_column_names() + result._feature_names = training_set.features.column_names result._target_name = training_set.target.name return result @@ -75,6 +86,12 @@ def predict(self, dataset: Table) -> TaggedTable: If the dataset misses feature columns. PredictionError If predicting with the given dataset failed. + NonNumericColumnError + If the dataset contains non-numerical values. + MissingValuesColumnError + If the dataset contains missing values. + DatasetMissesDataError + If the dataset contains no rows. """ return predict(self._wrapped_regressor, dataset, self._feature_names, self._target_name) @@ -88,3 +105,14 @@ def is_fitted(self) -> bool: Whether the regressor is fitted. """ return self._wrapped_regressor is not None + + def _get_sklearn_regressor(self) -> RegressorMixin: + """ + Return a new wrapped Regressor from sklearn. + + Returns + ------- + wrapped_regressor: RegressorMixin + The sklearn Regressor. + """ + return sk_LinearRegression(n_jobs=-1) diff --git a/src/safeds/ml/regression/_random_forest.py b/src/safeds/ml/classical/regression/_random_forest.py similarity index 54% rename from src/safeds/ml/regression/_random_forest.py rename to src/safeds/ml/classical/regression/_random_forest.py index 39fcee8bb..3908592ff 100644 --- a/src/safeds/ml/regression/_random_forest.py +++ b/src/safeds/ml/classical/regression/_random_forest.py @@ -4,22 +4,55 @@ from sklearn.ensemble import RandomForestRegressor as sk_RandomForestRegressor -from safeds.ml._util_sklearn import fit, predict +from safeds.ml.classical._util_sklearn import fit, predict from ._regressor import Regressor if TYPE_CHECKING: + from sklearn.base import RegressorMixin + from safeds.data.tabular.containers import Table, TaggedTable class RandomForest(Regressor): - """Random forest regression.""" + """Random forest regression. + + Parameters + ---------- + number_of_trees : int + The number of trees to be used in the random forest. Has to be greater than 0. + + Raises + ------ + ValueError + If `number_of_trees` is less than or equal to 0. + """ + + def __init__(self, *, number_of_trees: int = 100) -> None: + # Validation + if number_of_trees < 1: + raise ValueError("The parameter 'number_of_trees' has to be greater than 0.") + + # Hyperparameters + self._number_of_trees = number_of_trees - def __init__(self) -> None: + # Internal state self._wrapped_regressor: sk_RandomForestRegressor | None = None self._feature_names: list[str] | None = None self._target_name: str | None = None + @property + def number_of_trees(self) -> int: + """ + Get the number of trees used in the random forest. + + Returns + ------- + result: int + The number of trees. + """ + return self._number_of_trees + def fit(self, training_set: TaggedTable) -> RandomForest: """ Create a copy of this regressor and fit it with the given training data. @@ -40,13 +73,21 @@ def fit(self, training_set: TaggedTable) -> RandomForest: ------ LearningError If the training data contains invalid values or if the training failed. + UntaggedTableError + If the table is untagged. + NonNumericColumnError + If the training data contains non-numerical values. + MissingValuesColumnError + If the training data contains missing values. + DatasetMissesDataError + If the training data contains no rows. """ - wrapped_regressor = sk_RandomForestRegressor(n_jobs=-1) + wrapped_regressor = self._get_sklearn_regressor() fit(wrapped_regressor, training_set) - result = RandomForest() + result = RandomForest(number_of_trees=self._number_of_trees) result._wrapped_regressor = wrapped_regressor - result._feature_names = training_set.features.get_column_names() + result._feature_names = training_set.features.column_names result._target_name = training_set.target.name return result @@ -75,6 +116,12 @@ def predict(self, dataset: Table) -> TaggedTable: If the dataset misses feature columns. PredictionError If predicting with the given dataset failed. + NonNumericColumnError + If the dataset contains non-numerical values. + MissingValuesColumnError + If the dataset contains missing values. + DatasetMissesDataError + If the dataset contains no rows. """ return predict(self._wrapped_regressor, dataset, self._feature_names, self._target_name) @@ -88,3 +135,14 @@ def is_fitted(self) -> bool: Whether the regressor is fitted. """ return self._wrapped_regressor is not None + + def _get_sklearn_regressor(self) -> RegressorMixin: + """ + Return a new wrapped Regressor from sklearn. + + Returns + ------- + wrapped_regressor: RegressorMixin + The sklearn Regressor. + """ + return sk_RandomForestRegressor(self._number_of_trees, n_jobs=-1) diff --git a/src/safeds/ml/regression/_regressor.py b/src/safeds/ml/classical/regression/_regressor.py similarity index 81% rename from src/safeds/ml/regression/_regressor.py rename to src/safeds/ml/classical/regression/_regressor.py index 8bcb7b423..682781e58 100644 --- a/src/safeds/ml/regression/_regressor.py +++ b/src/safeds/ml/classical/regression/_regressor.py @@ -6,10 +6,11 @@ from sklearn.metrics import mean_absolute_error as sk_mean_absolute_error from sklearn.metrics import mean_squared_error as sk_mean_squared_error -from safeds.data.tabular.exceptions import ColumnLengthMismatchError +from safeds.data.tabular.containers import Column, Table, TaggedTable +from safeds.exceptions import ColumnLengthMismatchError, UntaggedTableError if TYPE_CHECKING: - from safeds.data.tabular.containers import Column, Table, TaggedTable + from sklearn.base import RegressorMixin class Regressor(ABC): @@ -76,6 +77,17 @@ def is_fitted(self) -> bool: Whether the regressor is fitted. """ + @abstractmethod + def _get_sklearn_regressor(self) -> RegressorMixin: + """ + Return a new wrapped Regressor from sklearn. + + Returns + ------- + wrapped_regressor: RegressorMixin + The sklearn Regressor. + """ + # noinspection PyProtectedMember def mean_squared_error(self, validation_or_test_set: TaggedTable) -> float: """ @@ -90,7 +102,14 @@ def mean_squared_error(self, validation_or_test_set: TaggedTable) -> float: ------- mean_squared_error : float The calculated mean squared error (the average of the distance of each individual row squared). + + Raises + ------ + UntaggedTableError + If the table is untagged. """ + if not isinstance(validation_or_test_set, TaggedTable) and isinstance(validation_or_test_set, Table): + raise UntaggedTableError expected = validation_or_test_set.target predicted = self.predict(validation_or_test_set.features).target @@ -111,7 +130,14 @@ def mean_absolute_error(self, validation_or_test_set: TaggedTable) -> float: ------- mean_absolute_error : float The calculated mean absolute error (the average of the distance of each individual row). + + Raises + ------ + UntaggedTableError + If the table is untagged. """ + if not isinstance(validation_or_test_set, TaggedTable) and isinstance(validation_or_test_set, Table): + raise UntaggedTableError expected = validation_or_test_set.target predicted = self.predict(validation_or_test_set.features).target diff --git a/src/safeds/ml/regression/_ridge_regression.py b/src/safeds/ml/classical/regression/_ridge_regression.py similarity index 51% rename from src/safeds/ml/regression/_ridge_regression.py rename to src/safeds/ml/classical/regression/_ridge_regression.py index 3dabbebef..b775c4215 100644 --- a/src/safeds/ml/regression/_ridge_regression.py +++ b/src/safeds/ml/classical/regression/_ridge_regression.py @@ -1,25 +1,69 @@ from __future__ import annotations +import warnings from typing import TYPE_CHECKING from sklearn.linear_model import Ridge as sk_Ridge -from safeds.ml._util_sklearn import fit, predict +from safeds.ml.classical._util_sklearn import fit, predict from ._regressor import Regressor if TYPE_CHECKING: + from sklearn.base import RegressorMixin + from safeds.data.tabular.containers import Table, TaggedTable class RidgeRegression(Regressor): - """Ridge regression.""" - - def __init__(self) -> None: + """ + Ridge regression. + + Parameters + ---------- + alpha : float + Controls the regularization of the model. The higher the value, the more regularized it becomes. + + Raises + ------ + ValueError + If `alpha` is negative. + """ + + def __init__(self, *, alpha: float = 1.0) -> None: + # Validation + if alpha < 0: + raise ValueError("The parameter 'alpha' must be non-negative") + if alpha == 0.0: + warnings.warn( + ( + "Setting alpha to zero makes this model equivalent to LinearRegression. You should use " + "LinearRegression instead for better numerical stability." + ), + UserWarning, + stacklevel=2, + ) + + # Hyperparameters + self._alpha = alpha + + # Internal state self._wrapped_regressor: sk_Ridge | None = None self._feature_names: list[str] | None = None self._target_name: str | None = None + @property + def alpha(self) -> float: + """ + Get the regularization of the model. + + Returns + ------- + result: float + The regularization of the model. + """ + return self._alpha + def fit(self, training_set: TaggedTable) -> RidgeRegression: """ Create a copy of this regressor and fit it with the given training data. @@ -40,13 +84,21 @@ def fit(self, training_set: TaggedTable) -> RidgeRegression: ------ LearningError If the training data contains invalid values or if the training failed. + UntaggedTableError + If the table is untagged. + NonNumericColumnError + If the training data contains non-numerical values. + MissingValuesColumnError + If the training data contains missing values. + DatasetMissesDataError + If the training data contains no rows. """ - wrapped_regressor = sk_Ridge() + wrapped_regressor = self._get_sklearn_regressor() fit(wrapped_regressor, training_set) - result = RidgeRegression() + result = RidgeRegression(alpha=self._alpha) result._wrapped_regressor = wrapped_regressor - result._feature_names = training_set.features.get_column_names() + result._feature_names = training_set.features.column_names result._target_name = training_set.target.name return result @@ -75,6 +127,12 @@ def predict(self, dataset: Table) -> TaggedTable: If the dataset misses feature columns. PredictionError If predicting with the given dataset failed. + NonNumericColumnError + If the dataset contains non-numerical values. + MissingValuesColumnError + If the dataset contains missing values. + DatasetMissesDataError + If the dataset contains no rows. """ return predict(self._wrapped_regressor, dataset, self._feature_names, self._target_name) @@ -88,3 +146,14 @@ def is_fitted(self) -> bool: Whether the regressor is fitted. """ return self._wrapped_regressor is not None + + def _get_sklearn_regressor(self) -> RegressorMixin: + """ + Return a new wrapped Regressor from sklearn. + + Returns + ------- + wrapped_regressor: RegressorMixin + The sklearn Regressor. + """ + return sk_Ridge(alpha=self._alpha) diff --git a/src/safeds/ml/classical/regression/_support_vector_machine.py b/src/safeds/ml/classical/regression/_support_vector_machine.py new file mode 100644 index 000000000..3fb6fe367 --- /dev/null +++ b/src/safeds/ml/classical/regression/_support_vector_machine.py @@ -0,0 +1,255 @@ +from __future__ import annotations + +from abc import ABC, abstractmethod +from typing import TYPE_CHECKING + +from sklearn.svm import SVR as sk_SVR # noqa: N811 + +from safeds.ml.classical._util_sklearn import fit, predict +from safeds.ml.classical.regression import Regressor + +if TYPE_CHECKING: + from sklearn.base import RegressorMixin + + from safeds.data.tabular.containers import Table, TaggedTable + + +class SupportVectorMachineKernel(ABC): + """The abstract base class of the different subclasses supported by the `Kernel`.""" + + @abstractmethod + def get_sklearn_kernel(self) -> object: + """ + Get the kernel of the given SupportVectorMachine. + + Returns + ------- + object + The kernel of the SupportVectorMachine. + """ + + +class SupportVectorMachine(Regressor): + """ + Support vector machine. + + Parameters + ---------- + c: float + The strength of regularization. Must be strictly positive. + kernel: The type of kernel to be used. Defaults to None. + + Raises + ------ + ValueError + If `c` is less than or equal to 0. + """ + + def __init__(self, *, c: float = 1.0, kernel: SupportVectorMachineKernel | None = None) -> None: + # Internal state + self._wrapped_regressor: sk_SVR | None = None + self._feature_names: list[str] | None = None + self._target_name: str | None = None + + # Hyperparameters + if c <= 0: + raise ValueError("The parameter 'c' has to be strictly positive.") + self._c = c + self._kernel = kernel + + @property + def c(self) -> float: + """ + Get the regularization strength. + + Returns + ------- + result: float + The regularization strength. + """ + return self._c + + @property + def kernel(self) -> SupportVectorMachineKernel | None: + """ + Get the type of kernel used. + + Returns + ------- + result: SupportVectorMachineKernel | None + The type of kernel used. + """ + return self._kernel + + class Kernel: + class Linear(SupportVectorMachineKernel): + def get_sklearn_kernel(self) -> str: + """ + Get the name of the linear kernel. + + Returns + ------- + result: str + The name of the linear kernel. + """ + return "linear" + + class Polynomial(SupportVectorMachineKernel): + def __init__(self, degree: int): + if degree < 1: + raise ValueError("The parameter 'degree' has to be greater than or equal to 1.") + self._degree = degree + + def get_sklearn_kernel(self) -> str: + """ + Get the name of the polynomial kernel. + + Returns + ------- + result: str + The name of the polynomial kernel. + """ + return "poly" + + class Sigmoid(SupportVectorMachineKernel): + def get_sklearn_kernel(self) -> str: + """ + Get the name of the sigmoid kernel. + + Returns + ------- + result: str + The name of the sigmoid kernel. + """ + return "sigmoid" + + class RadialBasisFunction(SupportVectorMachineKernel): + def get_sklearn_kernel(self) -> str: + """ + Get the name of the radial basis function (RBF) kernel. + + Returns + ------- + result: str + The name of the RBF kernel. + """ + return "rbf" + + def _get_kernel_name(self) -> str: + """ + Get the name of the kernel. + + Returns + ------- + result: str + The name of the kernel. + + Raises + ------ + TypeError + If the kernel type is invalid. + """ + if isinstance(self.kernel, SupportVectorMachine.Kernel.Linear): + return "linear" + elif isinstance(self.kernel, SupportVectorMachine.Kernel.Polynomial): + return "poly" + elif isinstance(self.kernel, SupportVectorMachine.Kernel.Sigmoid): + return "sigmoid" + elif isinstance(self.kernel, SupportVectorMachine.Kernel.RadialBasisFunction): + return "rbf" + else: + raise TypeError("Invalid kernel type.") + + def fit(self, training_set: TaggedTable) -> SupportVectorMachine: + """ + Create a copy of this regressor and fit it with the given training data. + + This regressor is not modified. + + Parameters + ---------- + training_set : TaggedTable + The training data containing the feature and target vectors. + + Returns + ------- + fitted_regressor : SupportVectorMachine + The fitted regressor. + + Raises + ------ + LearningError + If the training data contains invalid values or if the training failed. + UntaggedTableError + If the table is untagged. + NonNumericColumnError + If the training data contains non-numerical values. + MissingValuesColumnError + If the training data contains missing values. + DatasetMissesDataError + If the training data contains no rows. + """ + wrapped_regressor = self._get_sklearn_regressor() + fit(wrapped_regressor, training_set) + + result = SupportVectorMachine(c=self._c, kernel=self._kernel) + result._wrapped_regressor = wrapped_regressor + result._feature_names = training_set.features.column_names + result._target_name = training_set.target.name + + return result + + def predict(self, dataset: Table) -> TaggedTable: + """ + Predict a target vector using a dataset containing feature vectors. The model has to be trained first. + + Parameters + ---------- + dataset : Table + The dataset containing the feature vectors. + + Returns + ------- + table : TaggedTable + A dataset containing the given feature vectors and the predicted target vector. + + Raises + ------ + ModelNotFittedError + If the model has not been fitted yet. + DatasetContainsTargetError + If the dataset contains the target column already. + DatasetMissesFeaturesError + If the dataset misses feature columns. + PredictionError + If predicting with the given dataset failed. + NonNumericColumnError + If the dataset contains non-numerical values. + MissingValuesColumnError + If the dataset contains missing values. + DatasetMissesDataError + If the dataset contains no rows. + """ + return predict(self._wrapped_regressor, dataset, self._feature_names, self._target_name) + + def is_fitted(self) -> bool: + """ + Check if the regressor is fitted. + + Returns + ------- + is_fitted : bool + Whether the regressor is fitted. + """ + return self._wrapped_regressor is not None + + def _get_sklearn_regressor(self) -> RegressorMixin: + """ + Return a new wrapped Regressor from sklearn. + + Returns + ------- + wrapped_regressor: RegressorMixin + The sklearn Regressor. + """ + return sk_SVR(C=self._c) diff --git a/src/safeds/ml/classification/_ada_boost.py b/src/safeds/ml/classification/_ada_boost.py deleted file mode 100644 index df00200a9..000000000 --- a/src/safeds/ml/classification/_ada_boost.py +++ /dev/null @@ -1,90 +0,0 @@ -from __future__ import annotations - -from typing import TYPE_CHECKING - -from sklearn.ensemble import AdaBoostClassifier as sk_AdaBoostClassifier - -from safeds.ml._util_sklearn import fit, predict - -from ._classifier import Classifier - -if TYPE_CHECKING: - from safeds.data.tabular.containers import Table, TaggedTable - - -class AdaBoost(Classifier): - """Ada Boost classification.""" - - def __init__(self) -> None: - self._wrapped_classifier: sk_AdaBoostClassifier | None = None - self._feature_names: list[str] | None = None - self._target_name: str | None = None - - def fit(self, training_set: TaggedTable) -> AdaBoost: - """ - Create a copy of this classifier and fit it with the given training data. - - This classifier is not modified. - - Parameters - ---------- - training_set : TaggedTable - The training data containing the feature and target vectors. - - Returns - ------- - fitted_classifier : AdaBoost - The fitted classifier. - - Raises - ------ - LearningError - If the training data contains invalid values or if the training failed. - """ - wrapped_classifier = sk_AdaBoostClassifier() - fit(wrapped_classifier, training_set) - - result = AdaBoost() - result._wrapped_classifier = wrapped_classifier - result._feature_names = training_set.features.get_column_names() - result._target_name = training_set.target.name - - return result - - def predict(self, dataset: Table) -> TaggedTable: - """ - Predict a target vector using a dataset containing feature vectors. The model has to be trained first. - - Parameters - ---------- - dataset : Table - The dataset containing the feature vectors. - - Returns - ------- - table : TaggedTable - A dataset containing the given feature vectors and the predicted target vector. - - Raises - ------ - ModelNotFittedError - If the model has not been fitted yet. - DatasetContainsTargetError - If the dataset contains the target column already. - DatasetMissesFeaturesError - If the dataset misses feature columns. - PredictionError - If predicting with the given dataset failed. - """ - return predict(self._wrapped_classifier, dataset, self._feature_names, self._target_name) - - def is_fitted(self) -> bool: - """ - Check if the classifier is fitted. - - Returns - ------- - is_fitted : bool - Whether the classifier is fitted. - """ - return self._wrapped_classifier is not None diff --git a/src/safeds/ml/classification/_classifier.py b/src/safeds/ml/classification/_classifier.py deleted file mode 100644 index 3752c95a0..000000000 --- a/src/safeds/ml/classification/_classifier.py +++ /dev/null @@ -1,94 +0,0 @@ -from __future__ import annotations - -from abc import ABC, abstractmethod -from typing import TYPE_CHECKING - -from sklearn.metrics import accuracy_score as sk_accuracy_score - -if TYPE_CHECKING: - from safeds.data.tabular.containers import Table, TaggedTable - - -class Classifier(ABC): - """Abstract base class for all classifiers.""" - - @abstractmethod - def fit(self, training_set: TaggedTable) -> Classifier: - """ - Create a copy of this classifier and fit it with the given training data. - - This classifier is not modified. - - Parameters - ---------- - training_set : TaggedTable - The training data containing the feature and target vectors. - - Returns - ------- - fitted_classifier : Classifier - The fitted classifier. - - Raises - ------ - LearningError - If the training data contains invalid values or if the training failed. - """ - - @abstractmethod - def predict(self, dataset: Table) -> TaggedTable: - """ - Predict a target vector using a dataset containing feature vectors. The model has to be trained first. - - Parameters - ---------- - dataset : Table - The dataset containing the feature vectors. - - Returns - ------- - table : TaggedTable - A dataset containing the given feature vectors and the predicted target vector. - - Raises - ------ - ModelNotFittedError - If the model has not been fitted yet. - DatasetContainsTargetError - If the dataset contains the target column already. - DatasetMissesFeaturesError - If the dataset misses feature columns. - PredictionError - If predicting with the given dataset failed. - """ - - @abstractmethod - def is_fitted(self) -> bool: - """ - Check if the classifier is fitted. - - Returns - ------- - is_fitted : bool - Whether the classifier is fitted. - """ - - # noinspection PyProtectedMember - def accuracy(self, validation_or_test_set: TaggedTable) -> float: - """ - Compute the accuracy of the classifier on the given data. - - Parameters - ---------- - validation_or_test_set : TaggedTable - The validation or test set. - - Returns - ------- - accuracy : float - The calculated accuracy score, i.e. the percentage of equal data. - """ - expected = validation_or_test_set.target - predicted = self.predict(validation_or_test_set.features).target - - return sk_accuracy_score(expected._data, predicted._data) diff --git a/src/safeds/ml/classification/_gradient_boosting_classification.py b/src/safeds/ml/classification/_gradient_boosting_classification.py deleted file mode 100644 index 3dc7c1d7d..000000000 --- a/src/safeds/ml/classification/_gradient_boosting_classification.py +++ /dev/null @@ -1,90 +0,0 @@ -from __future__ import annotations - -from typing import TYPE_CHECKING - -from sklearn.ensemble import GradientBoostingClassifier as sk_GradientBoostingClassifier - -from safeds.ml._util_sklearn import fit, predict - -from ._classifier import Classifier - -if TYPE_CHECKING: - from safeds.data.tabular.containers import Table, TaggedTable - - -class GradientBoosting(Classifier): - """Gradient boosting classification.""" - - def __init__(self) -> None: - self._wrapped_classifier: sk_GradientBoostingClassifier | None = None - self._feature_names: list[str] | None = None - self._target_name: str | None = None - - def fit(self, training_set: TaggedTable) -> GradientBoosting: - """ - Create a copy of this classifier and fit it with the given training data. - - This classifier is not modified. - - Parameters - ---------- - training_set : TaggedTable - The training data containing the feature and target vectors. - - Returns - ------- - fitted_classifier : GradientBoosting - The fitted classifier. - - Raises - ------ - LearningError - If the training data contains invalid values or if the training failed. - """ - wrapped_classifier = sk_GradientBoostingClassifier() - fit(wrapped_classifier, training_set) - - result = GradientBoosting() - result._wrapped_classifier = wrapped_classifier - result._feature_names = training_set.features.get_column_names() - result._target_name = training_set.target.name - - return result - - def predict(self, dataset: Table) -> TaggedTable: - """ - Predict a target vector using a dataset containing feature vectors. The model has to be trained first. - - Parameters - ---------- - dataset : Table - The dataset containing the feature vectors. - - Returns - ------- - table : TaggedTable - A dataset containing the given feature vectors and the predicted target vector. - - Raises - ------ - ModelNotFittedError - If the model has not been fitted yet. - DatasetContainsTargetError - If the dataset contains the target column already. - DatasetMissesFeaturesError - If the dataset misses feature columns. - PredictionError - If predicting with the given dataset failed. - """ - return predict(self._wrapped_classifier, dataset, self._feature_names, self._target_name) - - def is_fitted(self) -> bool: - """ - Check if the classifier is fitted. - - Returns - ------- - is_fitted : bool - Whether the classifier is fitted. - """ - return self._wrapped_classifier is not None diff --git a/src/safeds/ml/exceptions/__init__.py b/src/safeds/ml/exceptions/__init__.py deleted file mode 100644 index c84e54ae2..000000000 --- a/src/safeds/ml/exceptions/__init__.py +++ /dev/null @@ -1,17 +0,0 @@ -"""Custom exceptions that can be raised when working with ML models.""" - -from ._exceptions import ( - DatasetContainsTargetError, - DatasetMissesFeaturesError, - LearningError, - ModelNotFittedError, - PredictionError, -) - -__all__ = [ - "DatasetContainsTargetError", - "DatasetMissesFeaturesError", - "LearningError", - "ModelNotFittedError", - "PredictionError", -] diff --git a/src/safeds/ml/hyperparameters/__init__.py b/src/safeds/ml/hyperparameters/__init__.py new file mode 100644 index 000000000..67291eba7 --- /dev/null +++ b/src/safeds/ml/hyperparameters/__init__.py @@ -0,0 +1,5 @@ +"""Tools to work with hyperparameters of ML models.""" + +from ._choice import Choice + +__all__ = ["Choice"] diff --git a/src/safeds/ml/hyperparameters/_choice.py b/src/safeds/ml/hyperparameters/_choice.py new file mode 100644 index 000000000..4eb612804 --- /dev/null +++ b/src/safeds/ml/hyperparameters/_choice.py @@ -0,0 +1,63 @@ +from __future__ import annotations + +from collections.abc import Collection +from typing import TYPE_CHECKING, TypeVar + +if TYPE_CHECKING: + from collections.abc import Iterator + from typing import Any + +T = TypeVar("T") + + +class Choice(Collection[T]): + """A list of values to choose from in a hyperparameter search.""" + + def __init__(self, *args: T) -> None: + """ + Create a new choice. + + Parameters + ---------- + *args: tuple[T, ...] + The values to choose from. + """ + self.elements = list(args) + + def __contains__(self, value: Any) -> bool: + """ + Check if a value is in this choice. + + Parameters + ---------- + value: Any + The value to check. + + Returns + ------- + is_in_choice : bool + Whether the value is in this choice. + """ + return value in self.elements + + def __iter__(self) -> Iterator[T]: + """ + Iterate over the values of this choice. + + Returns + ------- + iterator : Iterator[T] + An iterator over the values of this choice. + """ + return iter(self.elements) + + def __len__(self) -> int: + """ + Get the number of values in this choice. + + Returns + ------- + number_of_values : int + The number of values in this choice. + """ + return len(self.elements) diff --git a/src/safeds/ml/regression/_ada_boost.py b/src/safeds/ml/regression/_ada_boost.py deleted file mode 100644 index 4f2b6715d..000000000 --- a/src/safeds/ml/regression/_ada_boost.py +++ /dev/null @@ -1,90 +0,0 @@ -from __future__ import annotations - -from typing import TYPE_CHECKING - -from sklearn.ensemble import AdaBoostRegressor as sk_AdaBoostRegressor - -from safeds.ml._util_sklearn import fit, predict - -from ._regressor import Regressor - -if TYPE_CHECKING: - from safeds.data.tabular.containers import Table, TaggedTable - - -class AdaBoost(Regressor): - """Ada Boost regression.""" - - def __init__(self) -> None: - self._wrapped_regressor: sk_AdaBoostRegressor | None = None - self._feature_names: list[str] | None = None - self._target_name: str | None = None - - def fit(self, training_set: TaggedTable) -> AdaBoost: - """ - Create a copy of this regressor and fit it with the given training data. - - This regressor is not modified. - - Parameters - ---------- - training_set : TaggedTable - The training data containing the feature and target vectors. - - Returns - ------- - fitted_regressor : AdaBoost - The fitted regressor. - - Raises - ------ - LearningError - If the training data contains invalid values or if the training failed. - """ - wrapped_regressor = sk_AdaBoostRegressor() - fit(wrapped_regressor, training_set) - - result = AdaBoost() - result._wrapped_regressor = wrapped_regressor - result._feature_names = training_set.features.get_column_names() - result._target_name = training_set.target.name - - return result - - def predict(self, dataset: Table) -> TaggedTable: - """ - Predict a target vector using a dataset containing feature vectors. The model has to be trained first. - - Parameters - ---------- - dataset : Table - The dataset containing the feature vectors. - - Returns - ------- - table : TaggedTable - A dataset containing the given feature vectors and the predicted target vector. - - Raises - ------ - ModelNotFittedError - If the model has not been fitted yet. - DatasetContainsTargetError - If the dataset contains the target column already. - DatasetMissesFeaturesError - If the dataset misses feature columns. - PredictionError - If predicting with the given dataset failed. - """ - return predict(self._wrapped_regressor, dataset, self._feature_names, self._target_name) - - def is_fitted(self) -> bool: - """ - Check if the regressor is fitted. - - Returns - ------- - is_fitted : bool - Whether the regressor is fitted. - """ - return self._wrapped_regressor is not None diff --git a/src/safeds/ml/regression/_elastic_net_regression.py b/src/safeds/ml/regression/_elastic_net_regression.py deleted file mode 100644 index 824c50d0c..000000000 --- a/src/safeds/ml/regression/_elastic_net_regression.py +++ /dev/null @@ -1,90 +0,0 @@ -from __future__ import annotations - -from typing import TYPE_CHECKING - -from sklearn.linear_model import ElasticNet as sk_ElasticNet - -from safeds.ml._util_sklearn import fit, predict - -from ._regressor import Regressor - -if TYPE_CHECKING: - from safeds.data.tabular.containers import Table, TaggedTable - - -class ElasticNetRegression(Regressor): - """Elastic net regression.""" - - def __init__(self) -> None: - self._wrapped_regressor: sk_ElasticNet | None = None - self._feature_names: list[str] | None = None - self._target_name: str | None = None - - def fit(self, training_set: TaggedTable) -> ElasticNetRegression: - """ - Create a copy of this regressor and fit it with the given training data. - - This regressor is not modified. - - Parameters - ---------- - training_set : TaggedTable - The training data containing the feature and target vectors. - - Returns - ------- - fitted_regressor : ElasticNetRegression - The fitted regressor. - - Raises - ------ - LearningError - If the training data contains invalid values or if the training failed. - """ - wrapped_regressor = sk_ElasticNet() - fit(wrapped_regressor, training_set) - - result = ElasticNetRegression() - result._wrapped_regressor = wrapped_regressor - result._feature_names = training_set.features.get_column_names() - result._target_name = training_set.target.name - - return result - - def predict(self, dataset: Table) -> TaggedTable: - """ - Predict a target vector using a dataset containing feature vectors. The model has to be trained first. - - Parameters - ---------- - dataset : Table - The dataset containing the feature vectors. - - Returns - ------- - table : TaggedTable - A dataset containing the given feature vectors and the predicted target vector. - - Raises - ------ - ModelNotFittedError - If the model has not been fitted yet. - DatasetContainsTargetError - If the dataset contains the target column already. - DatasetMissesFeaturesError - If the dataset misses feature columns. - PredictionError - If predicting with the given dataset failed. - """ - return predict(self._wrapped_regressor, dataset, self._feature_names, self._target_name) - - def is_fitted(self) -> bool: - """ - Check if the regressor is fitted. - - Returns - ------- - is_fitted : bool - Whether the regressor is fitted. - """ - return self._wrapped_regressor is not None diff --git a/src/safeds/ml/regression/_gradient_boosting_regression.py b/src/safeds/ml/regression/_gradient_boosting_regression.py deleted file mode 100644 index a58030527..000000000 --- a/src/safeds/ml/regression/_gradient_boosting_regression.py +++ /dev/null @@ -1,90 +0,0 @@ -from __future__ import annotations - -from typing import TYPE_CHECKING - -from sklearn.ensemble import GradientBoostingRegressor as sk_GradientBoostingRegressor - -from safeds.ml._util_sklearn import fit, predict - -from ._regressor import Regressor - -if TYPE_CHECKING: - from safeds.data.tabular.containers import Table, TaggedTable - - -class GradientBoosting(Regressor): - """Gradient boosting regression.""" - - def __init__(self) -> None: - self._wrapped_regressor: sk_GradientBoostingRegressor | None = None - self._feature_names: list[str] | None = None - self._target_name: str | None = None - - def fit(self, training_set: TaggedTable) -> GradientBoosting: - """ - Create a copy of this regressor and fit it with the given training data. - - This regressor is not modified. - - Parameters - ---------- - training_set : TaggedTable - The training data containing the feature and target vectors. - - Returns - ------- - fitted_regressor : GradientBoosting - The fitted regressor. - - Raises - ------ - LearningError - If the training data contains invalid values or if the training failed. - """ - wrapped_regressor = sk_GradientBoostingRegressor() - fit(wrapped_regressor, training_set) - - result = GradientBoosting() - result._wrapped_regressor = wrapped_regressor - result._feature_names = training_set.features.get_column_names() - result._target_name = training_set.target.name - - return result - - def predict(self, dataset: Table) -> TaggedTable: - """ - Predict a target vector using a dataset containing feature vectors. The model has to be trained first. - - Parameters - ---------- - dataset : Table - The dataset containing the feature vectors. - - Returns - ------- - table : TaggedTable - A dataset containing the given feature vectors and the predicted target vector. - - Raises - ------ - ModelNotFittedError - If the model has not been fitted yet. - DatasetContainsTargetError - If the dataset contains the target column already. - DatasetMissesFeaturesError - If the dataset misses feature columns. - PredictionError - If predicting with the given dataset failed. - """ - return predict(self._wrapped_regressor, dataset, self._feature_names, self._target_name) - - def is_fitted(self) -> bool: - """ - Check if the regressor is fitted. - - Returns - ------- - is_fitted : bool - Whether the regressor is fitted. - """ - return self._wrapped_regressor is not None diff --git a/tests/helpers/__init__.py b/tests/helpers/__init__.py index 019d5cbe7..cbd8637a1 100644 --- a/tests/helpers/__init__.py +++ b/tests/helpers/__init__.py @@ -1,3 +1,4 @@ +from ._assertions import assert_that_tables_are_close, assert_that_tagged_tables_are_equal from ._resources import resolve_resource_path -__all__ = ["resolve_resource_path"] +__all__ = ["assert_that_tables_are_close", "assert_that_tagged_tables_are_equal", "resolve_resource_path"] diff --git a/tests/helpers/_assertions.py b/tests/helpers/_assertions.py new file mode 100644 index 000000000..2dcbcd1e0 --- /dev/null +++ b/tests/helpers/_assertions.py @@ -0,0 +1,41 @@ +import pytest +from safeds.data.tabular.containers import Table, TaggedTable + + +def assert_that_tables_are_close(table1: Table, table2: Table) -> None: + """ + Assert that two tables are almost equal. + + Parameters + ---------- + table1: Table + The first table. + table2: Table + The table to compare the first table to. + """ + assert table1.schema == table2.schema + for column_name in table1.column_names: + assert table1.get_column(column_name).type == table2.get_column(column_name).type + assert table1.get_column(column_name).type.is_numeric() + assert table2.get_column(column_name).type.is_numeric() + for i in range(table1.number_of_rows): + entry_1 = table1.get_column(column_name).get_value(i) + entry_2 = table2.get_column(column_name).get_value(i) + assert entry_1 == pytest.approx(entry_2) + + +def assert_that_tagged_tables_are_equal(table1: TaggedTable, table2: TaggedTable) -> None: + """ + Assert that two tagged tables are equal. + + Parameters + ---------- + table1: TaggedTable + The first table. + table2: TaggedTable + The table to compare the first table to. + """ + assert table1.schema == table2.schema + assert table1.features == table2.features + assert table1.target == table2.target + assert table1 == table2 diff --git a/tests/helpers/_resources.py b/tests/helpers/_resources.py index 2eb04de6f..5394cdb8b 100644 --- a/tests/helpers/_resources.py +++ b/tests/helpers/_resources.py @@ -3,13 +3,13 @@ _resources_root = Path(__file__).parent / ".." / "resources" -def resolve_resource_path(resource_path: str) -> str: +def resolve_resource_path(resource_path: str | Path) -> str: """ Resolve a path relative to the `resources` directory to an absolute path. Parameters ---------- - resource_path : str + resource_path : str | Path The path to the resource relative to the `resources` directory. 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a/tests/resources/test_column_table.csv b/tests/resources/test_column_table.csv deleted file mode 100644 index a4561c113..000000000 --- a/tests/resources/test_column_table.csv +++ /dev/null @@ -1,3 +0,0 @@ -A,B -1,2 -4,5 diff --git a/tests/resources/test_row_table.csv b/tests/resources/test_row_table.csv deleted file mode 100644 index 1a7bf9443..000000000 --- a/tests/resources/test_row_table.csv +++ /dev/null @@ -1,4 +0,0 @@ -A,B,D -1,4,d -2,5,e -3,6,f diff --git a/tests/resources/test_table.csv b/tests/resources/test_table.csv deleted file mode 100644 index 069620839..000000000 --- a/tests/resources/test_table.csv +++ /dev/null @@ -1,5 +0,0 @@ -A, 1234, dtyp(ab) -1, a, ( -2, b, ) -3, c, [ -4, d, ] diff --git a/tests/resources/test_table_add_column_valid_input.csv b/tests/resources/test_table_add_column_valid_input.csv deleted file mode 100644 index b8da46c12..000000000 --- a/tests/resources/test_table_add_column_valid_input.csv +++ /dev/null @@ -1,4 +0,0 @@ -A,B -1,2 -3,4 -5,6 diff --git a/tests/resources/test_table_add_column_valid_output.csv b/tests/resources/test_table_add_column_valid_output.csv deleted file mode 100644 index c2a3b19e5..000000000 --- a/tests/resources/test_table_add_column_valid_output.csv +++ /dev/null @@ -1,4 +0,0 @@ -A,B,C -1,2,a -3,4,b -5,6,c diff --git a/tests/resources/test_table_duplicate_rows_duplicates.csv b/tests/resources/test_table_duplicate_rows_duplicates.csv deleted file mode 100644 index 3d9b1e7da..000000000 --- a/tests/resources/test_table_duplicate_rows_duplicates.csv +++ /dev/null @@ -1,5 +0,0 @@ -A,B -1,2 -1,2 -1,2 -4,5 diff --git a/tests/resources/test_table_duplicate_rows_no_duplicates.csv b/tests/resources/test_table_duplicate_rows_no_duplicates.csv deleted file mode 100644 index a4561c113..000000000 --- a/tests/resources/test_table_duplicate_rows_no_duplicates.csv +++ /dev/null @@ -1,3 +0,0 @@ -A,B -1,2 -4,5 diff --git a/tests/resources/test_table_from_json_file.json b/tests/resources/test_table_from_json_file.json deleted file mode 100644 index 0a99ca9a0..000000000 --- a/tests/resources/test_table_from_json_file.json +++ /dev/null @@ -1,6 +0,0 @@ -[ - { - "A": 1, - "B": 2 - } -] diff --git a/tests/resources/test_table_has_column.csv b/tests/resources/test_table_has_column.csv deleted file mode 100644 index a3672abe3..000000000 --- a/tests/resources/test_table_has_column.csv +++ /dev/null @@ -1,2 +0,0 @@ -A,B -1,2 diff --git a/tests/resources/test_table_replace_column_input.csv b/tests/resources/test_table_replace_column_input.csv deleted file mode 100644 index 17769938c..000000000 --- a/tests/resources/test_table_replace_column_input.csv +++ /dev/null @@ -1,4 +0,0 @@ -A,B,C -1,4,a -2,5,b -3,6,c diff --git a/tests/resources/test_table_replace_column_output_different_name.csv b/tests/resources/test_table_replace_column_output_different_name.csv deleted file mode 100644 index bac766440..000000000 --- a/tests/resources/test_table_replace_column_output_different_name.csv +++ /dev/null @@ -1,4 +0,0 @@ -A,B,C -1,4,d -2,5,e -3,6,f diff --git a/tests/resources/test_table_replace_column_output_same_name.csv b/tests/resources/test_table_replace_column_output_same_name.csv deleted file mode 100644 index 1a7bf9443..000000000 --- a/tests/resources/test_table_replace_column_output_same_name.csv +++ /dev/null @@ -1,4 +0,0 @@ -A,B,D -1,4,d -2,5,e -3,6,f diff --git a/tests/resources/test_table_transform_column.csv b/tests/resources/test_table_transform_column.csv deleted file mode 100644 index 17769938c..000000000 --- a/tests/resources/test_table_transform_column.csv +++ /dev/null @@ -1,4 +0,0 @@ -A,B,C -1,4,a -2,5,b -3,6,c diff --git a/tests/resources/test_table_transform_column_output.csv b/tests/resources/test_table_transform_column_output.csv deleted file mode 100644 index 3812deed2..000000000 --- a/tests/resources/test_table_transform_column_output.csv +++ /dev/null @@ -1,4 +0,0 @@ -A,B,C -2,4,a -4,5,b -6,6,c diff --git a/tests/resources/white.jpg/white.png b/tests/resources/white.jpg/white.png new file mode 100644 index 000000000..4849863e8 Binary files /dev/null and b/tests/resources/white.jpg/white.png differ diff --git a/tests/safeds/data/image/containers/test_image.py b/tests/safeds/data/image/containers/test_image.py index 2c0d5b622..79af2a8e1 100644 --- a/tests/safeds/data/image/containers/test_image.py +++ b/tests/safeds/data/image/containers/test_image.py @@ -4,6 +4,7 @@ import pytest from safeds.data.image.containers import Image from safeds.data.image.typing import ImageFormat +from safeds.data.tabular.containers import Table from tests.helpers import resolve_resource_path @@ -11,16 +12,18 @@ class TestFromJpegFile: @pytest.mark.parametrize( "path", - ["image/white_square.jpg"], + ["image/white_square.jpg", Path("image/white_square.jpg")], + ids=["jpg", "jpg_Path"], ) - def test_should_load_jpeg_file(self, path: str) -> None: + def test_should_load_jpeg_file(self, path: str | Path) -> None: Image.from_jpeg_file(resolve_resource_path(path)) @pytest.mark.parametrize( "path", - ["image/missing_file.jpg"], + ["image/missing_file.jpg", Path("image/missing_file.jpg")], + ids=["missing_file_jpg", "missing_file_jpg_Path"], ) - def test_should_raise_if_file_not_found(self, path: str) -> None: + def test_should_raise_if_file_not_found(self, path: str | Path) -> None: with pytest.raises(FileNotFoundError): Image.from_jpeg_file(resolve_resource_path(path)) @@ -28,16 +31,18 @@ def test_should_raise_if_file_not_found(self, path: str) -> None: class TestFromPngFile: @pytest.mark.parametrize( "path", - ["image/white_square.png"], + ["image/white_square.png", Path("image/white_square.png")], + ids=["png", "png_Path"], ) - def test_should_load_png_file(self, path: str) -> None: + def test_should_load_png_file(self, path: str | Path) -> None: Image.from_png_file(resolve_resource_path(path)) @pytest.mark.parametrize( "path", - ["image/missing_file.png"], + ["image/missing_file.png", Path("image/missing_file.png")], + ids=["missing_file_png", "missing_file_png_Path"], ) - def test_should_raise_if_file_not_found(self, path: str) -> None: + def test_should_raise_if_file_not_found(self, path: str | Path) -> None: with pytest.raises(FileNotFoundError): Image.from_png_file(resolve_resource_path(path)) @@ -49,17 +54,37 @@ class TestFormat: (Image.from_jpeg_file(resolve_resource_path("image/white_square.jpg")), ImageFormat.JPEG), (Image.from_png_file(resolve_resource_path("image/white_square.png")), ImageFormat.PNG), ], + ids=["jpg", "png"], ) def test_should_return_correct_format(self, image: Image, format_: ImageFormat) -> None: assert image.format == format_ -class TestToJpegFile: +class TestProperties: @pytest.mark.parametrize( - "path", - ["image/white_square.jpg"], + ("image", "width", "height"), + [ + ( + Image.from_jpeg_file(resolve_resource_path("image/white_square.jpg")), + 1, + 1, + ), + ( + Image.from_png_file(resolve_resource_path("image/snapshot_boxplot.png")), + 640, + 480, + ), + ], + ids=["[1,1].jpg", "[640,480].png"], ) - def test_should_save_jpeg_file(self, path: str) -> None: + def test_should_return_image_properties(self, image: Image, width: int, height: int) -> None: + assert image.width == width + assert image.height == height + + +class TestToJpegFile: + @pytest.mark.parametrize("path", ["image/white_square.jpg"], ids=["jpg_file"]) + def test_should_save_jpeg_file_by_str(self, path: str) -> None: image = Image.from_jpeg_file(resolve_resource_path(path)) with NamedTemporaryFile() as tmp_file: @@ -71,13 +96,23 @@ def test_should_save_jpeg_file(self, path: str) -> None: assert image._image.tobytes() == image_read_back._image.tobytes() + @pytest.mark.parametrize("path", ["image/white_square.jpg"], ids=["jpg"]) + def test_should_save_jpeg_file_by_path(self, path: str) -> None: + image = Image.from_jpeg_file(resolve_resource_path(path)) + + with NamedTemporaryFile() as tmp_file: + tmp_file.close() + with Path(tmp_file.name).open("wb") as tmp_write_file: + image.to_jpeg_file(Path(tmp_write_file.name)) + with Path(tmp_file.name).open("rb") as tmp_read_file: + image_read_back = Image.from_jpeg_file(tmp_read_file.name) + + assert image._image.tobytes() == image_read_back._image.tobytes() + class TestToPngFile: - @pytest.mark.parametrize( - "path", - ["image/white_square.png"], - ) - def test_should_save_png_file(self, path: str) -> None: + @pytest.mark.parametrize("path", ["image/white_square.png"], ids=["png"]) + def test_should_save_png_file_by_str(self, path: str) -> None: image = Image.from_png_file(resolve_resource_path(path)) with NamedTemporaryFile() as tmp_file: @@ -89,11 +124,25 @@ def test_should_save_png_file(self, path: str) -> None: assert image._image.tobytes() == image_read_back._image.tobytes() + @pytest.mark.parametrize("path", ["image/white_square.png"], ids=["png"]) + def test_should_save_png_file_by_path(self, path: str) -> None: + image = Image.from_png_file(resolve_resource_path(path)) + + with NamedTemporaryFile() as tmp_file: + tmp_file.close() + with Path(tmp_file.name).open("wb") as tmp_write_file: + image.to_png_file(Path(tmp_write_file.name)) + with Path(tmp_file.name).open("rb") as tmp_read_file: + image_read_back = Image.from_png_file(tmp_read_file.name) + + assert image._image.tobytes() == image_read_back._image.tobytes() + class TestReprJpeg: @pytest.mark.parametrize( "image", [Image.from_jpeg_file(resolve_resource_path("image/white_square.jpg"))], + ids=["jpg"], ) def test_should_return_bytes_if_image_is_jpeg(self, image: Image) -> None: assert isinstance(image._repr_jpeg_(), bytes) @@ -101,6 +150,7 @@ def test_should_return_bytes_if_image_is_jpeg(self, image: Image) -> None: @pytest.mark.parametrize( "image", [Image.from_png_file(resolve_resource_path("image/white_square.png"))], + ids=["png"], ) def test_should_return_none_if_image_is_not_jpeg(self, image: Image) -> None: assert image._repr_jpeg_() is None @@ -110,6 +160,7 @@ class TestReprPng: @pytest.mark.parametrize( "image", [Image.from_png_file(resolve_resource_path("image/white_square.png"))], + ids=["png"], ) def test_should_return_bytes_if_image_is_png(self, image: Image) -> None: assert isinstance(image._repr_png_(), bytes) @@ -117,6 +168,311 @@ def test_should_return_bytes_if_image_is_png(self, image: Image) -> None: @pytest.mark.parametrize( "image", [Image.from_jpeg_file(resolve_resource_path("image/white_square.jpg"))], + ids=["jpg"], ) def test_should_return_none_if_image_is_not_png(self, image: Image) -> None: assert image._repr_png_() is None + + +class TestResize: + @pytest.mark.parametrize( + ("image", "new_width", "new_height", "new_size"), + [ + ( + Image.from_png_file(resolve_resource_path("image/white_square.png")), + 2, + 3, + (2, 3), + ), + ], + ids=["(2, 3)"], + ) + def test_should_return_resized_image( + self, + image: Image, + new_width: int, + new_height: int, + new_size: tuple[int, int], + ) -> None: + assert image.resize(new_width, new_height)._image.size == new_size + + +class TestConvertToGrayscale: + @pytest.mark.parametrize( + ("image", "expected"), + [ + ( + Image.from_png_file(resolve_resource_path("image/snapshot_heatmap.png")), + Image.from_png_file(resolve_resource_path("image/snapshot_heatmap_grayscale.png")), + ), + ], + ids=["grayscale"], + ) + def test_convert_to_grayscale(self, image: Image, expected: Image) -> None: + grayscale_image = image.convert_to_grayscale() + assert grayscale_image == expected + + +class TestEQ: + def test_should_be_equal(self) -> None: + image = Image.from_png_file(resolve_resource_path("image/original.png")) + image2 = Image.from_png_file(resolve_resource_path("image/copy.png")) + assert image == image2 + + def test_should_not_be_equal(self) -> None: + image = Image.from_png_file(resolve_resource_path("image/original.png")) + image2 = Image.from_png_file(resolve_resource_path("image/white_square.png")) + assert image != image2 + + def test_should_raise(self) -> None: + image = Image.from_png_file(resolve_resource_path("image/original.png")) + other = Table() + assert (image.__eq__(other)) is NotImplemented + + +class TestFlipVertically: + def test_should_flip_vertically(self) -> None: + image = Image.from_png_file(resolve_resource_path("image/original.png")) + image2 = image.flip_vertically() + image3 = Image.from_png_file(resolve_resource_path("image/flip_vertically.png")) + assert image != image2 + assert image2 == image3 + + def test_should_be_original(self) -> None: + image = Image.from_png_file(resolve_resource_path("image/original.png")) + image2 = image.flip_vertically().flip_vertically() + assert image == image2 + + +class TestFlipHorizontally: + def test_should_flip_horizontally(self) -> None: + image = Image.from_png_file(resolve_resource_path("image/original.png")) + image2 = image.flip_horizontally() + image3 = Image.from_png_file(resolve_resource_path("image/flip_horizontally.png")) + assert image != image2 + assert image2 == image3 + + def test_should_be_original(self) -> None: + image = Image.from_png_file(resolve_resource_path("image/original.png")) + image2 = image.flip_horizontally().flip_horizontally() + assert image == image2 + + +class TestAdjustContrast: + @pytest.mark.parametrize("factor", [0.75, 5], ids=["small factor", "large factor"]) + def test_should_adjust_contrast(self, factor: float) -> None: + image = Image.from_png_file(resolve_resource_path("image/contrast/to_adjust_contrast.png")) + image2 = image.adjust_contrast(factor) + image3 = Image.from_png_file( + resolve_resource_path("image/contrast/contrast_adjusted_by_" + str(factor) + ".png"), + ) + assert image != image2 + assert image2 == image3 + + def test_should_not_adjust_contrast(self) -> None: + with pytest.warns( + UserWarning, + match="Contrast adjustment factor is 1.0, this will not make changes to the image.", + ): + image = Image.from_png_file(resolve_resource_path("image/contrast/to_adjust_contrast.png")) + image2 = image.adjust_contrast(1) + assert image == image2 + + def test_should_raise(self) -> None: + image = Image.from_png_file(resolve_resource_path("image/brightness/to_brighten.png")) + with pytest.raises(ValueError, match="Contrast factor has to be 0 or bigger"): + image.adjust_contrast(-1) + + +class TestBrightness: + @pytest.mark.parametrize("factor", [0.5, 10], ids=["small factor", "large factor"]) + def test_should_adjust_brightness(self, factor: float) -> None: + image = Image.from_png_file(resolve_resource_path("image/brightness/to_brighten.png")) + image2 = image.adjust_brightness(factor) + image3 = Image.from_png_file(resolve_resource_path("image/brightness/brightened_by_" + str(factor) + ".png")) + assert image != image2 + assert image2 == image3 + + def test_should_not_brighten(self) -> None: + with pytest.warns( + UserWarning, + match="Brightness adjustment factor is 1.0, this will not make changes to the image.", + ): + image = Image.from_png_file(resolve_resource_path("image/brightness/to_brighten.png")) + image2 = image.adjust_brightness(1) + assert image == image2 + + def test_should_raise(self) -> None: + image = Image.from_png_file(resolve_resource_path("image/brightness/to_brighten.png")) + with pytest.raises(ValueError, match="Brightness factor has to be 0 or bigger"): + image.adjust_brightness(-1) + + +class TestInvertColors: + @pytest.mark.parametrize( + ("image", "expected"), + [ + ( + Image.from_png_file(resolve_resource_path("image/original.png")), + Image.from_png_file(resolve_resource_path("image/inverted_colors_original.png")), + ), + ], + ids=["invert-colors"], + ) + def test_should_invert_colors(self, image: Image, expected: Image) -> None: + image = image.invert_colors() + assert image == expected + + +class TestColorAdjust: + @pytest.mark.parametrize( + ("image", "factor", "expected"), + [ + ( + Image.from_png_file(resolve_resource_path("image/original.png")), + 2, + Image.from_png_file(resolve_resource_path("image/adjusted_colors/by_2.png")), + ), + ( + Image.from_png_file(resolve_resource_path("image/original.png")), + 0.5, + Image.from_png_file(resolve_resource_path("image/adjusted_colors/by_0.5.png")), + ), + ( + Image.from_png_file(resolve_resource_path("image/original.png")), + 0, + Image.from_png_file(resolve_resource_path("image/adjusted_colors/by_0.png")), + ), + ], + ids=["add color", "remove color", "remove all color"], + ) + def test_should_adjust_colors(self, image: Image, factor: float, expected: Image) -> None: + image = image.adjust_color_balance(factor) + assert image == expected + + @pytest.mark.parametrize( + ("image", "factor"), + [ + ( + Image.from_png_file(resolve_resource_path("image/original.png")), + -1, + ), + ], + ids=["negative"], + ) + def test_should_throw(self, image: Image, factor: float) -> None: + with pytest.raises(ValueError, match="Color factor has to be 0 or bigger."): + image.adjust_color_balance(factor) + + @pytest.mark.parametrize( + ("image", "factor"), + [ + ( + Image.from_png_file(resolve_resource_path("image/original.png")), + 1, + ), + ], + ids=["no change"], + ) + def test_should_warn(self, image: Image, factor: float) -> None: + with pytest.warns( + UserWarning, + match="Color adjustment factor is 1.0, this will not make changes to the image.", + ): + adjust = image.adjust_color_balance(factor) + assert adjust == image + + +class TestBlur: + @pytest.mark.parametrize( + ("image", "expected"), + [ + ( + Image.from_png_file(resolve_resource_path("image/boy.png")), + Image.from_png_file(resolve_resource_path("image/blurredBoy.png")), + ), + ], + ids=["blur"], + ) + def test_should_return_blurred_image(self, image: Image, expected: Image) -> None: + image = image.blur(2) + assert image == expected + + +class TestCrop: + @pytest.mark.parametrize( + ("image", "expected"), + [ + ( + Image.from_png_file(resolve_resource_path("image/white.png")), + Image.from_png_file(resolve_resource_path("image/whiteCropped.png")), + ), + ], + ids=["crop"], + ) + def test_should_return_cropped_image(self, image: Image, expected: Image) -> None: + image = image.crop(0, 0, 100, 100) + assert image == expected + + +class TestSharpen: + @pytest.mark.parametrize("factor", [-1, 0.5, 10], ids=["negative factor", "small factor", "large factor"]) + def test_should_sharpen(self, factor: float) -> None: + image = Image.from_png_file(resolve_resource_path("image/sharpen/to_sharpen.png")) + image2 = image.sharpen(factor) + image2.to_png_file(resolve_resource_path("image/sharpen/sharpened_by_" + str(factor) + ".png")) + assert image != image2 + assert image2 == Image.from_png_file( + resolve_resource_path("image/sharpen/sharpened_by_" + str(factor) + ".png"), + ) + + def test_should_not_sharpen(self) -> None: + image = Image.from_png_file(resolve_resource_path("image/sharpen/to_sharpen.png")) + image2 = image.sharpen(1) + assert image == image2 + + +class TestRotate: + @pytest.mark.parametrize( + ("image", "expected"), + [ + ( + Image.from_png_file(resolve_resource_path("image/snapshot_boxplot.png")), + Image.from_png_file(resolve_resource_path("image/snapshot_boxplot_right_rotation.png")), + ), + ], + ids=["rotate-clockwise"], + ) + def test_should_return_clockwise_rotated_image(self, image: Image, expected: Image) -> None: + image = image.rotate_right() + assert image == expected + + @pytest.mark.parametrize( + ("image", "expected"), + [ + ( + Image.from_png_file(resolve_resource_path("image/snapshot_boxplot.png")), + Image.from_png_file(resolve_resource_path("image/snapshot_boxplot_left_rotation.png")), + ), + ], + ids=["rotate-counter-clockwise"], + ) + def test_should_return_counter_clockwise_rotated_image(self, image: Image, expected: Image) -> None: + image = image.rotate_left() + assert image == expected + + +class TestFindEdges: + @pytest.mark.parametrize( + ("image", "expected"), + [ + ( + Image.from_png_file(resolve_resource_path("image/boy.png")), + Image.from_png_file(resolve_resource_path("image/edgyBoy.png")), + ), + ], + ids=["find_edges"], + ) + def test_should_return_edges_of_image(self, image: Image, expected: Image) -> None: + image = image.find_edges() + assert image == expected diff --git a/tests/safeds/data/tabular/containers/_column/test_all.py b/tests/safeds/data/tabular/containers/_column/test_all.py new file mode 100644 index 000000000..7a6cca3a2 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_column/test_all.py @@ -0,0 +1,25 @@ +from collections.abc import Callable + +import pytest +from safeds.data.tabular.containers import Column + + +@pytest.mark.parametrize( + ("column", "predicate", "expected"), + [ + (Column("a", []), lambda value: value == 1, True), + (Column("a", [1]), lambda value: value == 1, True), + (Column("a", [1, 2]), lambda value: value == 1, False), + ], + ids=[ + "empty", + "predicate always true", + "predicate sometimes false", + ], +) +def test_should_return_true_if_all_values_satisfy_the_predicate( + column: Column, + predicate: Callable, + expected: bool, +) -> None: + assert column.all(predicate) == expected diff --git a/tests/safeds/data/tabular/containers/_column/test_any.py b/tests/safeds/data/tabular/containers/_column/test_any.py new file mode 100644 index 000000000..b49992f5b --- /dev/null +++ b/tests/safeds/data/tabular/containers/_column/test_any.py @@ -0,0 +1,25 @@ +from collections.abc import Callable + +import pytest +from safeds.data.tabular.containers import Column + + +@pytest.mark.parametrize( + ("column", "predicate", "expected"), + [ + (Column("a", []), lambda value: value == 1, False), + (Column("a", [1]), lambda value: value == 1, True), + (Column("a", [1, 2]), lambda value: value == 1, True), + ], + ids=[ + "empty", + "predicate always true", + "predicate sometimes true", + ], +) +def test_should_return_true_if_any_values_satisfy_the_predicate( + column: Column, + predicate: Callable, + expected: bool, +) -> None: + assert column.any(predicate) == expected diff --git a/tests/safeds/data/tabular/containers/_column/test_boxplot.py b/tests/safeds/data/tabular/containers/_column/test_boxplot.py deleted file mode 100644 index 841cb6891..000000000 --- a/tests/safeds/data/tabular/containers/_column/test_boxplot.py +++ /dev/null @@ -1,29 +0,0 @@ -import _pytest -import matplotlib.pyplot as plt -import pytest -from safeds.data.tabular.containers import Table -from safeds.data.tabular.exceptions import NonNumericColumnError - - -def test_boxplot_complex() -> None: - with pytest.raises(TypeError): # noqa: PT012 - table = Table.from_dict({"A": [1, 2, complex(1, -2)]}) - table.get_column("A").boxplot() - - -def test_boxplot_non_numeric() -> None: - table = Table.from_dict({"A": [1, 2, "A"]}) - with pytest.raises(NonNumericColumnError): - table.get_column("A").boxplot() - - -def test_boxplot_float(monkeypatch: _pytest.monkeypatch) -> None: - monkeypatch.setattr(plt, "show", lambda: None) - table = Table.from_dict({"A": [1, 2, 3.5]}) - table.get_column("A").boxplot() - - -def test_boxplot_int(monkeypatch: _pytest.monkeypatch) -> None: - monkeypatch.setattr(plt, "show", lambda: None) - table = Table.from_dict({"A": [1, 2, 3]}) - table.get_column("A").boxplot() diff --git a/tests/safeds/data/tabular/containers/_column/test_column.py b/tests/safeds/data/tabular/containers/_column/test_column.py deleted file mode 100644 index 29d3af791..000000000 --- a/tests/safeds/data/tabular/containers/_column/test_column.py +++ /dev/null @@ -1,15 +0,0 @@ -from safeds.data.tabular.containers import Column - - -def test_from_columns() -> None: - column1 = Column("A", [1, 4]) - column2 = Column("B", [2, 5]) - - assert column1._type == column2._type - - -def test_from_columns_negative() -> None: - column1 = Column("A", [1, 4]) - column2 = Column("B", ["2", "5"]) - - assert column1._type != column2._type diff --git a/tests/safeds/data/tabular/containers/_column/test_column_properties.py b/tests/safeds/data/tabular/containers/_column/test_column_properties.py deleted file mode 100644 index e370d5b65..000000000 --- a/tests/safeds/data/tabular/containers/_column/test_column_properties.py +++ /dev/null @@ -1,31 +0,0 @@ -from safeds.data.tabular.containers import Column - - -def test_column_property_all_positive() -> None: - column = Column("col1", [1, 1, 1]) - assert column.all(lambda value: value == 1) - - -def test_column_property_all_negative() -> None: - column = Column("col1", [1, 2, 1]) - assert not column.all(lambda value: value == 1) - - -def test_column_property_any_positive() -> None: - column = Column("col1", [1, 2, 1]) - assert column.any(lambda value: value == 1) - - -def test_column_property_any_negative() -> None: - column = Column("col1", [1, 2, 1]) - assert not column.any(lambda value: value == 3) - - -def test_column_property_none_positive() -> None: - column = Column("col1", [1, 2, 1]) - assert column.none(lambda value: value == 3) - - -def test_column_property_none_negative() -> None: - column = Column("col1", [1, 2, 1]) - assert not column.none(lambda value: value == 1) diff --git a/tests/safeds/data/tabular/containers/_column/test_contains.py b/tests/safeds/data/tabular/containers/_column/test_contains.py new file mode 100644 index 000000000..b84a16432 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_column/test_contains.py @@ -0,0 +1,21 @@ +from typing import Any + +import pytest +from safeds.data.tabular.containers import Column + + +@pytest.mark.parametrize( + ("column", "value", "expected"), + [ + (Column("a", []), 1, False), + (Column("a", [1, 2, 3]), 1, True), + (Column("a", [1, 2, 3]), 4, False), + ], + ids=[ + "empty", + "value exists", + "value does not exist", + ], +) +def test_should_check_whether_the_value_exists(column: Column, value: Any, expected: bool) -> None: + assert (value in column) == expected diff --git a/tests/safeds/data/tabular/containers/_column/test_copy.py b/tests/safeds/data/tabular/containers/_column/test_copy.py new file mode 100644 index 000000000..cb2ef9df3 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_column/test_copy.py @@ -0,0 +1,13 @@ +import pytest +from safeds.data.tabular.containers import Column + + +@pytest.mark.parametrize( + "column", + [Column("a"), Column("a", ["a", 3, 0.1])], + ids=["empty", "normal"], +) +def test_should_copy_table(column: Column) -> None: + copied = column._copy() + assert copied == column + assert copied is not column diff --git a/tests/safeds/data/tabular/containers/_column/test_correlation_with.py b/tests/safeds/data/tabular/containers/_column/test_correlation_with.py index de7c88eb9..310b60adc 100644 --- a/tests/safeds/data/tabular/containers/_column/test_correlation_with.py +++ b/tests/safeds/data/tabular/containers/_column/test_correlation_with.py @@ -1,24 +1,39 @@ import pytest from safeds.data.tabular.containers import Column -from safeds.data.tabular.exceptions import ColumnLengthMismatchError, NonNumericColumnError +from safeds.exceptions import ColumnLengthMismatchError, NonNumericColumnError -def test_correlation_with() -> None: - column1 = Column("A", [1, 2, 3, 4]) - column2 = Column("B", [2, 3, 4, 5]) - actual_corr = column1.correlation_with(column2) - expected_corr = column1._data.corr(column2._data) - assert actual_corr == expected_corr +@pytest.mark.parametrize( + ("column1", "column2", "expected"), + [ + (Column("A", [0, 1, 2]), Column("B", [0, 1, 2]), 1.0), + (Column("A", [0, 1, 2]), Column("B", [0, -1, -2]), -1.0), + ], + ids=["positive correlation", "negative correlation"], +) +def test_should_return_correlation_between_two_columns(column1: Column, column2: Column, expected: float) -> None: + assert column1.correlation_with(column2) == expected -def test_correlation_with_raises_if_column_is_not_numeric() -> None: - column1 = Column("A", [1, 2, 3, 4]) - column2 = Column("B", ["a", "b", "c", "d"]) +@pytest.mark.parametrize( + ("column1", "column2"), + [ + (Column("A", [1]), Column("B", ["b"])), + (Column("A", ["a"]), Column("B", [2.0])), + (Column("A", ["a"]), Column("B", ["a"])), + ], + ids=[ + "first column is not numeric", + "second column is not numeric", + "both columns are not numeric", + ], +) +def test_should_raise_if_columns_are_not_numeric(column1: Column, column2: Column) -> None: with pytest.raises(NonNumericColumnError): column1.correlation_with(column2) -def test_correlation_with_raises_if_column_lengths_differ() -> None: +def test_should_raise_if_column_lengths_differ() -> None: column1 = Column("A", [1, 2, 3, 4]) column2 = Column("B", [2]) with pytest.raises(ColumnLengthMismatchError): diff --git a/tests/safeds/data/tabular/containers/_column/test_count.py b/tests/safeds/data/tabular/containers/_column/test_count.py deleted file mode 100644 index fa0808f91..000000000 --- a/tests/safeds/data/tabular/containers/_column/test_count.py +++ /dev/null @@ -1,12 +0,0 @@ -import pytest -from safeds.data.tabular.containers import Column - - -@pytest.mark.parametrize( - ("column", "expected"), - [ - (Column("col1", [1, 2, 3, 4, 5]), 5), - ], -) -def test_count_valid(column: Column, expected: int) -> None: - assert column.count() == expected diff --git a/tests/safeds/data/tabular/containers/_column/test_count_missing_values.py b/tests/safeds/data/tabular/containers/_column/test_count_missing_values.py new file mode 100644 index 000000000..b583fa3bb --- /dev/null +++ b/tests/safeds/data/tabular/containers/_column/test_count_missing_values.py @@ -0,0 +1,12 @@ +import pytest +from safeds.data.tabular.containers import Column + + +@pytest.mark.parametrize( + ("values", "expected"), + [([1, 2, 3], 0), ([1, 2, 3, None], 1), ([None, None, None], 3)], + ids=["no missing values", "some missing values", "all missing values"], +) +def test_should_count_missing_values(values: list, expected: float) -> None: + column = Column("A", values) + assert column._count_missing_values() == expected diff --git a/tests/safeds/data/tabular/containers/_column/test_count_null_values.py b/tests/safeds/data/tabular/containers/_column/test_count_null_values.py deleted file mode 100644 index 9321e09ca..000000000 --- a/tests/safeds/data/tabular/containers/_column/test_count_null_values.py +++ /dev/null @@ -1,13 +0,0 @@ -import numpy as np -from safeds.data.tabular.containers import Table - - -def test_count_null_values_valid() -> None: - table = Table.from_dict({"col1": [1, 2, 3, 4, 5], "col2": [None, None, 1, np.nan, np.nan]}) - empty_table = Table.from_dict({"col1": []}) - column1 = table.get_column("col1") - column2 = table.get_column("col2") - empty_column = empty_table.get_column("col1") - assert column1._count_missing_values() == 0 - assert column2._count_missing_values() == 4 - assert empty_column._count_missing_values() == 0 diff --git a/tests/safeds/data/tabular/containers/_column/test_eq.py b/tests/safeds/data/tabular/containers/_column/test_eq.py new file mode 100644 index 000000000..a4e4e04e5 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_column/test_eq.py @@ -0,0 +1,55 @@ +from typing import Any + +import pytest +from safeds.data.tabular.containers import Column, Row + + +@pytest.mark.parametrize( + ("column1", "column2", "expected"), + [ + (Column("a"), Column("a"), True), + (Column("a", [1, 2, 3]), Column("a", [1, 2, 3]), True), + (Column("a"), Column("b"), False), + (Column("a", [1, 2, 3]), Column("a", [1, 2, 4]), False), + (Column("a", [1, 2, 3]), Column("a", ["1", "2", "3"]), False), + ], + ids=[ + "empty columns", + "equal columns", + "different names", + "different values", + "different types", + ], +) +def test_should_return_whether_two_columns_are_equal(column1: Column, column2: Column, expected: bool) -> None: + assert (column1.__eq__(column2)) == expected + + +@pytest.mark.parametrize( + "column", + [ + Column("a"), + Column("a", [1, 2, 3]), + ], + ids=[ + "empty", + "non-empty", + ], +) +def test_should_return_true_if_objects_are_identical(column: Column) -> None: + assert (column.__eq__(column)) is True + + +@pytest.mark.parametrize( + ("column", "other"), + [ + (Column("a"), None), + (Column("a", [1, 2, 3]), Row()), + ], + ids=[ + "Column vs. None", + "Column vs. Row", + ], +) +def test_should_return_not_implemented_if_other_is_not_column(column: Column, other: Any) -> None: + assert (column.__eq__(other)) is NotImplemented diff --git a/tests/safeds/data/tabular/containers/_column/test_from_pandas_series.py b/tests/safeds/data/tabular/containers/_column/test_from_pandas_series.py new file mode 100644 index 000000000..9946120b2 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_column/test_from_pandas_series.py @@ -0,0 +1,48 @@ +import pandas as pd +import pytest +from safeds.data.tabular.containers import Column +from safeds.data.tabular.typing import Boolean, ColumnType, Integer, RealNumber, String + + +@pytest.mark.parametrize( + ("series", "expected"), + [ + (pd.Series([]), []), + (pd.Series([True, False, True]), [True, False, True]), + (pd.Series([1, 2, 3]), [1, 2, 3]), + (pd.Series([1.0, 2.0, 3.0]), [1.0, 2.0, 3.0]), + (pd.Series(["a", "b", "c"]), ["a", "b", "c"]), + (pd.Series([1, 2.0, "a", True]), [1, 2.0, "a", True]), + ], + ids=["empty", "boolean", "integer", "real number", "string", "mixed"], +) +def test_should_store_the_data(series: pd.Series, expected: Column) -> None: + assert list(Column._from_pandas_series(series)) == expected + + +@pytest.mark.parametrize( + ("series", "type_"), + [ + (pd.Series([True, False, True]), Boolean()), + (pd.Series([1, 2, 3]), Boolean()), + ], + ids=["type is correct", "type is wrong"], +) +def test_should_use_type_if_passed(series: pd.Series, type_: ColumnType) -> None: + assert Column._from_pandas_series(series, type_).type == type_ + + +@pytest.mark.parametrize( + ("series", "expected"), + [ + (pd.Series([]), String()), + (pd.Series([True, False, True]), Boolean()), + (pd.Series([1, 2, 3]), Integer()), + (pd.Series([1.0, 2.0, 3.0]), RealNumber()), + (pd.Series(["a", "b", "c"]), String()), + (pd.Series([1, 2.0, "a", True]), String()), + ], + ids=["empty", "boolean", "integer", "real number", "string", "mixed"], +) +def test_should_infer_type_if_not_passed(series: pd.Series, expected: ColumnType) -> None: + assert Column._from_pandas_series(series).type == expected diff --git a/tests/safeds/data/tabular/containers/_column/test_get_unique_values.py b/tests/safeds/data/tabular/containers/_column/test_get_unique_values.py index 0763731c7..9a433725f 100644 --- a/tests/safeds/data/tabular/containers/_column/test_get_unique_values.py +++ b/tests/safeds/data/tabular/containers/_column/test_get_unique_values.py @@ -11,10 +11,19 @@ @pytest.mark.parametrize( ("values", "unique_values"), - [([1, 1, 2, 3], [1, 2, 3]), (["a", "b", "b", "c"], ["a", "b", "c"]), ([], [])], + [ + ([], []), + ([1, 2, 3], [1, 2, 3]), + ([1, 1, 2, 3], [1, 2, 3]), + (["a", "b", "b", "c"], ["a", "b", "c"]), + ], + ids=[ + "empty", + "no duplicates", + "integer duplicates", + "string duplicates", + ], ) -def test_get_unique_values(values: list[Any], unique_values: list[Any]) -> None: +def test_should_list_unique_values(values: list[Any], unique_values: list[Any]) -> None: column: Column = Column("", values) - extracted_unique_values = column.get_unique_values() - - assert extracted_unique_values == unique_values + assert column.get_unique_values() == unique_values diff --git a/tests/safeds/data/tabular/containers/_column/test_get_value.py b/tests/safeds/data/tabular/containers/_column/test_get_value.py index e82521fd3..342ec8fb4 100644 --- a/tests/safeds/data/tabular/containers/_column/test_get_value.py +++ b/tests/safeds/data/tabular/containers/_column/test_get_value.py @@ -1,18 +1,32 @@ +from typing import Any + import pytest from safeds.data.tabular.containers import Column -from safeds.data.tabular.exceptions import IndexOutOfBoundsError +from safeds.exceptions import IndexOutOfBoundsError -def test_get_value_valid() -> None: - column = Column("testColumn", [0, "1"]) - assert column.get_value(0) == 0 - assert column.get_value(1) == "1" +@pytest.mark.parametrize( + ("column", "index", "expected"), + [ + (Column("a", [0, 1]), 0, 0), + (Column("a", [0, 1]), 1, 1), + ], + ids=["first item", "second item"], +) +def test_should_get_the_item_at_index(column: Column, index: int, expected: Any) -> None: + assert column.get_value(index) == expected -def test_get_value_invalid() -> None: - column = Column("testColumn", [0, "1"]) - with pytest.raises(IndexOutOfBoundsError): - column.get_value(-1) +@pytest.mark.parametrize( + "index", + [-1, 2], + ids=[ + "negative", + "out of bounds", + ], +) +def test_should_raise_if_index_is_out_of_bounds(index: int) -> None: + column = Column("a", [0, "1"]) with pytest.raises(IndexOutOfBoundsError): - column.get_value(2) + column.get_value(index) diff --git a/tests/safeds/data/tabular/containers/_column/test_getitem.py b/tests/safeds/data/tabular/containers/_column/test_getitem.py index 2ae9d3a2b..fe29f0af2 100644 --- a/tests/safeds/data/tabular/containers/_column/test_getitem.py +++ b/tests/safeds/data/tabular/containers/_column/test_getitem.py @@ -1,19 +1,50 @@ +from typing import Any + import pytest from safeds.data.tabular.containers import Column -from safeds.data.tabular.exceptions import IndexOutOfBoundsError +from safeds.exceptions import IndexOutOfBoundsError -def test_getitem_valid() -> None: - column = Column("testColumn", [0, "1"]) - assert column[0] == 0 - assert column[1] == "1" +@pytest.mark.parametrize( + ("column", "index", "expected"), + [ + (Column("a", [0, 1]), 0, 0), + (Column("a", [0, 1]), 1, 1), + ], + ids=["first item", "second item"], +) +def test_should_get_the_item_at_index(column: Column, index: int, expected: Any) -> None: + assert column[index] == expected -# noinspection PyStatementEffect -def test_getitem_invalid() -> None: - column = Column("testColumn", [0, "1"]) - with pytest.raises(IndexOutOfBoundsError): - column[-1] +@pytest.mark.parametrize( + ("column", "index", "expected"), + [ + (Column("a", [0, 1, 2]), slice(0, 1), Column("a", [0])), + (Column("a", [0, 1, 2]), slice(2, 3), Column("a", [2])), + (Column("a", [0, 1, 2]), slice(0, 3), Column("a", [0, 1, 2])), + (Column("a", [0, 1, 2]), slice(0, 3, 2), Column("a", [0, 2])), + ], + ids=["first item", "last item", "all items", "every other item"], +) +def test_should_get_the_items_at_slice(column: Column, index: slice, expected: Column) -> None: + assert column[index] == expected + + +@pytest.mark.parametrize( + "index", + [-1, 2, slice(-1, 2), slice(0, 4), slice(-1, 4)], + ids=[ + "negative", + "out of bounds", + "slice with negative start", + "slice with out of bounds end", + "slice with negative start and out of bounds end", + ], +) +def test_should_raise_if_index_is_out_of_bounds(index: int | slice) -> None: + column = Column("a", [0, "1"]) with pytest.raises(IndexOutOfBoundsError): - column[2] + # noinspection PyStatementEffect + column[index] diff --git a/tests/safeds/data/tabular/containers/_column/test_has_missing_values.py b/tests/safeds/data/tabular/containers/_column/test_has_missing_values.py index dd1c426ff..ae98cd92b 100644 --- a/tests/safeds/data/tabular/containers/_column/test_has_missing_values.py +++ b/tests/safeds/data/tabular/containers/_column/test_has_missing_values.py @@ -5,15 +5,18 @@ @pytest.mark.parametrize( ("values", "expected"), [ + ([], False), ([1, 2, 3], False), ([1, 2, 3, None], True), ([None, None, None], True), - ([], False), + ], + ids=[ + "empty", + "no missing values", + "some missing values", + "all missing values", ], ) -def test_has_missing_values(values: list, expected: bool) -> None: - if len(values) == 0: - column = Column("A", values) - else: - column = Column("A", values) +def test_should_return_whether_the_column_has_missing_values(values: list, expected: bool) -> None: + column = Column("A", values) assert column.has_missing_values() == expected diff --git a/tests/safeds/data/tabular/containers/_column/test_histogram.py b/tests/safeds/data/tabular/containers/_column/test_histogram.py deleted file mode 100644 index dd6fc4f05..000000000 --- a/tests/safeds/data/tabular/containers/_column/test_histogram.py +++ /dev/null @@ -1,9 +0,0 @@ -import _pytest -import matplotlib.pyplot as plt -from safeds.data.tabular.containers import Table - - -def test_histogram(monkeypatch: _pytest.monkeypatch) -> None: - monkeypatch.setattr(plt, "show", lambda: None) - table = Table.from_dict({"A": [1, 2, 3]}) - table.get_column("A").histogram() diff --git a/tests/safeds/data/tabular/containers/_column/test_idness.py b/tests/safeds/data/tabular/containers/_column/test_idness.py index 13fe5fa44..b1deba798 100644 --- a/tests/safeds/data/tabular/containers/_column/test_idness.py +++ b/tests/safeds/data/tabular/containers/_column/test_idness.py @@ -1,19 +1,29 @@ +from typing import Any + import pytest from safeds.data.tabular.containers import Column -from safeds.data.tabular.exceptions import ColumnSizeError +from safeds.exceptions import ColumnSizeError @pytest.mark.parametrize( ("values", "result"), - [(["A", "B"], 1), (["A", "A", "A", "B"], 0.5)], + [ + (["A", "B"], 1), + (["A", "A", "A", "B"], 0.5), + (["A", "A", "A", "A"], 0.25), + ], + ids=[ + "all unique values", + "some unique values", + "all same values", + ], ) -def test_idness_valid(values: list[str], result: float) -> None: - column = Column("test_idness_valid", values) - idness = column.idness() - assert idness == result +def test_should_return_idness_of_column(values: list[str], result: float) -> None: + column = Column("A", values) + assert column.idness() == result -def test_idness_invalid() -> None: - column = Column("test_idness_invalid", []) +def test_should_raise_if_column_is_empty() -> None: + column: Column[Any] = Column("A", []) with pytest.raises(ColumnSizeError): column.idness() diff --git a/tests/safeds/data/tabular/containers/_column/test_init.py b/tests/safeds/data/tabular/containers/_column/test_init.py new file mode 100644 index 000000000..2966b2970 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_column/test_init.py @@ -0,0 +1,56 @@ +from typing import Any + +import pandas as pd +import pytest +from safeds.data.tabular.containers import Column +from safeds.data.tabular.typing import Boolean, ColumnType, Integer, RealNumber, String + + +def test_should_store_the_name() -> None: + column: Column[Any] = Column("A", []) + assert column.name == "A" + + +@pytest.mark.parametrize( + ("column", "expected"), + [ + (Column("A", []), "A"), + (Column("A", pd.Series()), "A"), + ], + ids=["data as list", "data as series"], +) +def test_should_set_the_name_of_internal_series(column: Column, expected: str) -> None: + assert column._data.name == expected + + +@pytest.mark.parametrize( + ("column", "expected"), + [ + (Column("A"), []), + (Column("A", []), []), + (Column("A", [1, 2, 3]), [1, 2, 3]), + ], + ids=[ + "empty", + "empty (explicit)", + "non-empty", + ], +) +def test_should_store_the_data(column: Column, expected: list) -> None: + assert list(column) == expected + + +@pytest.mark.parametrize( + ("column", "expected"), + [ + (Column("A", []), String()), + (Column("A", [True, False, True]), Boolean()), + (Column("A", [1, 2, 3]), Integer()), + (Column("A", [1.0, 2.0, 3.0]), RealNumber()), + (Column("A", ["a", "b", "c"]), String()), + (Column("A", [1, 2.0, "a", True]), String()), + ], + ids=["empty", "boolean", "integer", "real number", "string", "mixed"], +) +def test_should_infer_type(column: Column, expected: ColumnType) -> None: + assert column.type == expected diff --git a/tests/safeds/data/tabular/containers/_column/test_iter.py b/tests/safeds/data/tabular/containers/_column/test_iter.py index 41775831e..c0568369b 100644 --- a/tests/safeds/data/tabular/containers/_column/test_iter.py +++ b/tests/safeds/data/tabular/containers/_column/test_iter.py @@ -1,6 +1,19 @@ +import pytest from safeds.data.tabular.containers import Column -def test_iter() -> None: - column = Column("testColumn", [0, "1"]) - assert list(column) == [0, "1"] +@pytest.mark.parametrize( + ("column", "expected"), + [ + (Column("a", []), []), + (Column("a", [0]), [0]), + (Column("a", [0, "1"]), [0, "1"]), + ], + ids=[ + "empty", + "one row", + "multiple rows", + ], +) +def test_should_iterate_over_the_data(column: Column, expected: list) -> None: + assert list(column) == expected diff --git a/tests/safeds/data/tabular/containers/_column/test_len.py b/tests/safeds/data/tabular/containers/_column/test_len.py index 8fd70ce16..7c14a596c 100644 --- a/tests/safeds/data/tabular/containers/_column/test_len.py +++ b/tests/safeds/data/tabular/containers/_column/test_len.py @@ -1,6 +1,19 @@ +import pytest from safeds.data.tabular.containers import Column -def test_count_valid() -> None: - column = Column("col1", [1, 2, 3, 4, 5]) - assert len(column) == 5 +@pytest.mark.parametrize( + ("column", "expected"), + [ + (Column("a", []), 0), + (Column("a", [0]), 1), + (Column("a", [0, "1"]), 2), + ], + ids=[ + "empty", + "one row", + "multiple rows", + ], +) +def test_should_return_the_number_of_rows(column: Column, expected: int) -> None: + assert len(column) == expected diff --git a/tests/safeds/data/tabular/containers/_column/test_maximum.py b/tests/safeds/data/tabular/containers/_column/test_maximum.py index e1ea12b6c..7988a0eab 100644 --- a/tests/safeds/data/tabular/containers/_column/test_maximum.py +++ b/tests/safeds/data/tabular/containers/_column/test_maximum.py @@ -1,16 +1,38 @@ import pytest -from safeds.data.tabular.containers import Table -from safeds.data.tabular.exceptions import NonNumericColumnError +from safeds.data.tabular.containers import Column +from safeds.exceptions import NonNumericColumnError -def test_maximum_invalid() -> None: - table = Table.from_dict({"col1": ["col1_1", 2]}) - column = table.get_column("col1") - with pytest.raises(NonNumericColumnError): - column.maximum() +@pytest.mark.parametrize( + ("values", "expected"), + [ + ([1, 2, 3], 3), + ([1, 2, 3, None], 3), + ], + ids=[ + "no missing values", + "some missing values", + ], +) +def test_should_return_the_maximum_value(values: list, expected: int) -> None: + column = Column("A", values) + assert column.maximum() == expected -def test_maximum_valid() -> None: - table = Table.from_dict({"col1": [1, 2, 3, 4]}) - column = table.get_column("col1") - assert column.maximum() == 4 +@pytest.mark.parametrize( + "values", + [ + [], + ["a", "b", "c"], + [None, None, None], + ], + ids=[ + "empty", + "non-numeric", + "all missing values", + ], +) +def test_should_raise_if_column_is_not_numeric(values: list) -> None: + column = Column("A", values) + with pytest.raises(NonNumericColumnError): + column.maximum() diff --git a/tests/safeds/data/tabular/containers/_column/test_mean.py b/tests/safeds/data/tabular/containers/_column/test_mean.py index 036628c8d..7b4859be3 100644 --- a/tests/safeds/data/tabular/containers/_column/test_mean.py +++ b/tests/safeds/data/tabular/containers/_column/test_mean.py @@ -1,16 +1,38 @@ import pytest -from safeds.data.tabular.containers import Table -from safeds.data.tabular.exceptions import NonNumericColumnError +from safeds.data.tabular.containers import Column +from safeds.exceptions import NonNumericColumnError -def test_mean_invalid() -> None: - table = Table.from_dict({"col1": ["col1_1", 2]}) - column = table.get_column("col1") - with pytest.raises(NonNumericColumnError): - column.mean() +@pytest.mark.parametrize( + ("values", "expected"), + [ + ([1, 2, 3], 2), + ([1, 2, 3, None], 2), + ], + ids=[ + "no missing values", + "some missing values", + ], +) +def test_should_return_the_mean_value(values: list, expected: int) -> None: + column = Column("A", values) + assert column.mean() == expected -def test_mean_valid() -> None: - table = Table.from_dict({"col1": [1, 2, 3, 4]}) - column = table.get_column("col1") - assert column.mean() == 2.5 +@pytest.mark.parametrize( + "values", + [ + [], + ["a", "b", "c"], + [None, None, None], + ], + ids=[ + "empty", + "non-numeric", + "all missing values", + ], +) +def test_should_raise_if_column_is_not_numeric(values: list) -> None: + column = Column("A", values) + with pytest.raises(NonNumericColumnError): + column.mean() diff --git a/tests/safeds/data/tabular/containers/_column/test_median.py b/tests/safeds/data/tabular/containers/_column/test_median.py index 9c21a78a3..7d81b15df 100644 --- a/tests/safeds/data/tabular/containers/_column/test_median.py +++ b/tests/safeds/data/tabular/containers/_column/test_median.py @@ -1,22 +1,40 @@ import pytest -from safeds.data.tabular.containers import Table -from safeds.data.tabular.exceptions import NonNumericColumnError +from safeds.data.tabular.containers import Column +from safeds.exceptions import NonNumericColumnError -def test_median_invalid() -> None: - table = Table.from_dict({"col1": ["col1_1", 2]}) - column = table.get_column("col1") - with pytest.raises(NonNumericColumnError): - column.median() - +@pytest.mark.parametrize( + ("values", "expected"), + [ + ([1, 2, 3], 2), + ([1, 2, 3, 4], 2.5), + ([1, 2, 3, None], 2), + ], + ids=[ + "odd number of values", + "even number of values", + "some missing values", + ], +) +def test_should_return_the_median_value(values: list, expected: int) -> None: + column = Column("A", values) + assert column.median() == expected -def test_median_valid() -> None: - table = Table.from_dict({"col1": [1, 2, 3, 4, 5]}) - column = table.get_column("col1") - assert column.median() == 3 - -def test_median_valid_between_two_values() -> None: - table = Table.from_dict({"col1": [1, 2, 3, 4, 5, 6]}) - column = table.get_column("col1") - assert column.median() == 3.5 +@pytest.mark.parametrize( + "values", + [ + [], + ["a", "b", "c"], + [None, None, None], + ], + ids=[ + "empty", + "non-numeric", + "all missing values", + ], +) +def test_should_raise_if_column_is_not_numeric(values: list) -> None: + column = Column("A", values) + with pytest.raises(NonNumericColumnError): + column.median() diff --git a/tests/safeds/data/tabular/containers/_column/test_minimum.py b/tests/safeds/data/tabular/containers/_column/test_minimum.py index a621a04ec..d8afe4bcc 100644 --- a/tests/safeds/data/tabular/containers/_column/test_minimum.py +++ b/tests/safeds/data/tabular/containers/_column/test_minimum.py @@ -1,16 +1,38 @@ import pytest -from safeds.data.tabular.containers import Table -from safeds.data.tabular.exceptions import NonNumericColumnError +from safeds.data.tabular.containers import Column +from safeds.exceptions import NonNumericColumnError -def test_minimum_invalid() -> None: - table = Table.from_dict({"col1": ["col1_1", 2]}) - column = table.get_column("col1") - with pytest.raises(NonNumericColumnError): - column.minimum() +@pytest.mark.parametrize( + ("values", "expected"), + [ + ([1, 2, 3], 1), + ([1, 2, 3, None], 1), + ], + ids=[ + "no missing values", + "some missing values", + ], +) +def test_should_return_the_minimum_value(values: list, expected: int) -> None: + column = Column("A", values) + assert column.minimum() == expected -def test_minimum_valid() -> None: - table = Table.from_dict({"col1": [1, 2, 3, 4]}) - column = table.get_column("col1") - assert column.minimum() == 1 +@pytest.mark.parametrize( + "values", + [ + [], + ["a", "b", "c"], + [None, None, None], + ], + ids=[ + "empty", + "non-numeric", + "all missing values", + ], +) +def test_should_raise_if_column_is_not_numeric(values: list) -> None: + column = Column("A", values) + with pytest.raises(NonNumericColumnError): + column.minimum() diff --git a/tests/safeds/data/tabular/containers/_column/test_missing_value_ratio.py b/tests/safeds/data/tabular/containers/_column/test_missing_value_ratio.py index 9d2409fc5..3d8856663 100644 --- a/tests/safeds/data/tabular/containers/_column/test_missing_value_ratio.py +++ b/tests/safeds/data/tabular/containers/_column/test_missing_value_ratio.py @@ -1,19 +1,21 @@ +from typing import Any + import pytest from safeds.data.tabular.containers import Column -from safeds.data.tabular.exceptions import ColumnSizeError +from safeds.exceptions import ColumnSizeError @pytest.mark.parametrize( ("values", "expected"), [([1, 2, 3], 0), ([1, 2, 3, None], 1 / 4), ([None, None, None], 1)], + ids=["no missing values", "some missing values", "all missing values"], ) -def test_missing_value_ratio(values: list, expected: float) -> None: +def test_should_return_ratio_of_null_values_to_number_of_elements(values: list, expected: float) -> None: column = Column("A", values) - result = column.missing_value_ratio() - assert result == expected + assert column.missing_value_ratio() == expected -def test_missing_value_ratio_empty() -> None: - column = Column("A", []) +def test_should_raise_if_column_is_empty() -> None: + column: Column[Any] = Column("A", []) with pytest.raises(ColumnSizeError): column.missing_value_ratio() diff --git a/tests/safeds/data/tabular/containers/_column/test_mode.py b/tests/safeds/data/tabular/containers/_column/test_mode.py index fce665eef..420b95181 100644 --- a/tests/safeds/data/tabular/containers/_column/test_mode.py +++ b/tests/safeds/data/tabular/containers/_column/test_mode.py @@ -1,19 +1,26 @@ -from safeds.data.tabular.containers import Table +import pytest +from safeds.data.tabular.containers import Column -def test_mode_valid() -> None: - table = Table.from_dict({"col1": [1, 2, 3, 4, 3]}) - column = table.get_column("col1") - assert column.mode() == [3] - - -def test_mode_valid_str() -> None: - table = Table.from_dict({"col1": ["1", "2", "3", "4", "3"]}) - column = table.get_column("col1") - assert column.mode() == ["3"] - - -def test_mode_valid_list() -> None: - table = Table.from_dict({"col1": ["1", "4", "3", "4", "3"]}) - column = table.get_column("col1") - assert column.mode() == ["3", "4"] +@pytest.mark.parametrize( + ("values", "expected"), + [ + ([], []), + ([None, None, None], []), + ([1, 2, 2], [2]), + (["a", "a", "b"], ["a"]), + ([1, 2, 2, None], [2]), + ([1, 2], [1, 2]), + ], + ids=[ + "empty", + "all missing values", + "numeric", + "non-numeric", + "some missing values", + "multiple values with same frequency", + ], +) +def test_should_return_the_mode_value(values: list, expected: list) -> None: + column = Column("A", values) + assert column.mode() == expected diff --git a/tests/safeds/data/tabular/containers/_column/test_none.py b/tests/safeds/data/tabular/containers/_column/test_none.py new file mode 100644 index 000000000..ee86255f0 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_column/test_none.py @@ -0,0 +1,27 @@ +from collections.abc import Callable + +import pytest +from safeds.data.tabular.containers import Column + + +@pytest.mark.parametrize( + ("column", "predicate", "expected"), + [ + (Column("a", []), lambda value: value == 1, True), + (Column("a", [1]), lambda value: value == 1, False), + (Column("a", [1, 2]), lambda value: value == 1, False), + (Column("a", [2, 2]), lambda value: value == 1, True), + ], + ids=[ + "empty", + "predicate always true", + "predicate sometimes true", + "predicate always false", + ], +) +def test_should_return_true_if_no_values_satisfy_the_predicate( + column: Column, + predicate: Callable, + expected: bool, +) -> None: + assert column.none(predicate) == expected diff --git a/tests/safeds/data/tabular/containers/_column/test_number_of_rows.py b/tests/safeds/data/tabular/containers/_column/test_number_of_rows.py new file mode 100644 index 000000000..df2e4089c --- /dev/null +++ b/tests/safeds/data/tabular/containers/_column/test_number_of_rows.py @@ -0,0 +1,19 @@ +import pytest +from safeds.data.tabular.containers import Column + + +@pytest.mark.parametrize( + ("column", "expected"), + [ + (Column("a", []), 0), + (Column("a", [0]), 1), + (Column("a", [0, "1"]), 2), + ], + ids=[ + "empty", + "one row", + "multiple rows", + ], +) +def test_should_return_the_number_of_rows(column: Column, expected: int) -> None: + assert column.number_of_rows == expected diff --git a/tests/safeds/data/tabular/containers/_column/test_plot_boxplot.py b/tests/safeds/data/tabular/containers/_column/test_plot_boxplot.py new file mode 100644 index 000000000..54b57b74c --- /dev/null +++ b/tests/safeds/data/tabular/containers/_column/test_plot_boxplot.py @@ -0,0 +1,22 @@ +import pytest +from safeds.data.image.containers import Image +from safeds.data.tabular.containers import Table +from safeds.exceptions import NonNumericColumnError + +from tests.helpers import resolve_resource_path + + +def test_should_match_snapshot() -> None: + table = Table({"A": [1, 2, 3]}) + current = table.get_column("A").plot_boxplot() + snapshot = Image.from_png_file(resolve_resource_path("./image/snapshot_boxplot.png")) + + # Inlining the expression into the assert causes pytest to hang if the assertion fails when run from PyCharm. + assertion = snapshot._image.tobytes() == current._image.tobytes() + assert assertion + + +def test_should_raise_if_column_contains_non_numerical_values() -> None: + table = Table({"A": [1, 2, "A"]}) + with pytest.raises(NonNumericColumnError): + table.get_column("A").plot_boxplot() diff --git a/tests/safeds/data/tabular/containers/_column/test_plot_histogram.py b/tests/safeds/data/tabular/containers/_column/test_plot_histogram.py new file mode 100644 index 000000000..fe4157b6a --- /dev/null +++ b/tests/safeds/data/tabular/containers/_column/test_plot_histogram.py @@ -0,0 +1,21 @@ +from safeds.data.image.containers import Image +from safeds.data.tabular.containers import Table + +from tests.helpers import resolve_resource_path + + +def test_should_match_snapshot_numeric() -> None: + table = Table({"A": [1, 2, 3]}) + current = table.get_column("A").plot_histogram() + snapshot = Image.from_png_file(resolve_resource_path("./image/snapshot_histogram_numeric.png")) + + # Inlining the expression into the assert causes pytest to hang if the assertion fails when run from PyCharm. + assertion = snapshot._image.tobytes() == current._image.tobytes() + assert assertion + + +def test_should_match_snapshot_str() -> None: + table = Table({"A": ["A", "B", "Apple"]}) + current = table.get_column("A").plot_histogram() + snapshot = Image.from_png_file(resolve_resource_path("./image/snapshot_histogram_str.png")) + assert snapshot._image.tobytes() == current._image.tobytes() diff --git a/tests/safeds/data/tabular/containers/_column/test_rename.py b/tests/safeds/data/tabular/containers/_column/test_rename.py index beffe0d8e..4c0d38585 100644 --- a/tests/safeds/data/tabular/containers/_column/test_rename.py +++ b/tests/safeds/data/tabular/containers/_column/test_rename.py @@ -5,9 +5,11 @@ def test_should_return_new_column_with_new_name() -> None: column = Column("A", [1, 2, 3]) new_column = column.rename("B") assert new_column.name == "B" + assert new_column._data.name == "B" def test_should_not_change_name_of_original_column() -> None: column = Column("A", [1, 2, 3]) column.rename("B") assert column.name == "A" + assert column._data.name == "A" diff --git a/tests/safeds/data/tabular/containers/_column/test_repr.py b/tests/safeds/data/tabular/containers/_column/test_repr.py new file mode 100644 index 000000000..2cd696012 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_column/test_repr.py @@ -0,0 +1,19 @@ +import pytest +from safeds.data.tabular.containers import Column + + +@pytest.mark.parametrize( + ("column", "expected"), + [ + (Column("a", []), "Column('a', [])"), + (Column("a", [0]), "Column('a', [0])"), + (Column("a", [0, "1"]), "Column('a', [0, '1'])"), + ], + ids=[ + "empty", + "one row", + "multiple rows", + ], +) +def test_should_return_a_string_representation(column: Column, expected: str) -> None: + assert repr(column) == expected diff --git a/tests/safeds/data/tabular/containers/_column/test_repr_html.py b/tests/safeds/data/tabular/containers/_column/test_repr_html.py new file mode 100644 index 000000000..0f94355a6 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_column/test_repr_html.py @@ -0,0 +1,50 @@ +import pytest +import regex as re +from safeds.data.tabular.containers import Column + + +@pytest.mark.parametrize( + "column", + [ + Column("a", []), + Column("a", [1, 2, 3]), + ], + ids=[ + "empty", + "non-empty", + ], +) +def test_should_contain_table_element(column: Column) -> None: + pattern = r".*?" + assert re.search(pattern, column._repr_html_(), flags=re.S) is not None + + +@pytest.mark.parametrize( + "column", + [ + Column("a", []), + Column("a", [1, 2, 3]), + ], + ids=[ + "empty", + "non-empty", + ], +) +def test_should_contain_th_element_for_column_name(column: Column) -> None: + assert f"{column.name}" in column._repr_html_() + + +@pytest.mark.parametrize( + "column", + [ + Column("a", []), + Column("a", [1, 2, 3]), + ], + ids=[ + "empty", + "non-empty", + ], +) +def test_should_contain_td_element_for_each_value(column: Column) -> None: + for value in column: + assert f"{value}" in column._repr_html_() diff --git a/tests/safeds/data/tabular/containers/_column/test_stability.py b/tests/safeds/data/tabular/containers/_column/test_stability.py index 01cf5a3eb..abb9f346f 100644 --- a/tests/safeds/data/tabular/containers/_column/test_stability.py +++ b/tests/safeds/data/tabular/containers/_column/test_stability.py @@ -2,23 +2,36 @@ import pytest from safeds.data.tabular.containers import Column -from safeds.data.tabular.exceptions import ColumnSizeError +from safeds.exceptions import ColumnSizeError @pytest.mark.parametrize( ("values", "expected"), [ + ([1, 2, 3, 4], 1 / 4), ([1, 2, 3, 4, None], 1 / 4), - ([1, 1, 3, "abc", None], 2 / 4), (["b", "a", "abc", "abc", "abc"], 3 / 5), + ([1, 1, 3, "abc", None], 2 / 4), + ], + ids=[ + "numeric", + "numeric with missing values", + "non-numeric", + "mixed with missing values", ], ) -def test_stability(values: list[Any], expected: float) -> None: +def test_should_return_stability_of_column(values: list[Any], expected: float) -> None: column = Column("A", values) assert column.stability() == expected -def test_stability_error() -> None: - column = Column("A", []) - with pytest.raises(ColumnSizeError): +def test_should_raise_column_size_error_if_column_is_empty() -> None: + column: Column[Any] = Column("A", []) + with pytest.raises(ColumnSizeError, match="Expected a column of size > 0 but got column of size 0."): + column.stability() + + +def test_should_raise_value_error_if_column_contains_only_none() -> None: + column: Column[Any] = Column("A", [None, None]) + with pytest.raises(ValueError, match="Stability is not definded for a column with only null values."): column.stability() diff --git a/tests/safeds/data/tabular/containers/_column/test_standard_deviation.py b/tests/safeds/data/tabular/containers/_column/test_standard_deviation.py new file mode 100644 index 000000000..48aaf0e4a --- /dev/null +++ b/tests/safeds/data/tabular/containers/_column/test_standard_deviation.py @@ -0,0 +1,38 @@ +import pytest +from safeds.data.tabular.containers import Column +from safeds.exceptions import NonNumericColumnError + + +@pytest.mark.parametrize( + ("values", "expected"), + [ + ([1, 2, 3], 1), + ([1, 2, 3, None], 1), + ], + ids=[ + "no missing values", + "some missing values", + ], +) +def test_should_return_standard_deviation(values: list, expected: int) -> None: + column = Column("A", values) + assert column.standard_deviation() == expected + + +@pytest.mark.parametrize( + "values", + [ + [], + ["a", "b", "c"], + [None, None, None], + ], + ids=[ + "empty", + "non-numeric", + "all missing values", + ], +) +def test_should_raise_if_column_is_not_numeric(values: list) -> None: + column = Column("A", values) + with pytest.raises(NonNumericColumnError): + column.standard_deviation() diff --git a/tests/safeds/data/tabular/containers/_column/test_str.py b/tests/safeds/data/tabular/containers/_column/test_str.py new file mode 100644 index 000000000..e0f84661e --- /dev/null +++ b/tests/safeds/data/tabular/containers/_column/test_str.py @@ -0,0 +1,19 @@ +import pytest +from safeds.data.tabular.containers import Column + + +@pytest.mark.parametrize( + ("column", "expected"), + [ + (Column("a", []), "'a': []"), + (Column("a", [0]), "'a': [0]"), + (Column("a", [0, "1"]), "'a': [0, '1']"), + ], + ids=[ + "empty", + "one row", + "multiple rows", + ], +) +def test_should_return_a_string_representation(column: Column, expected: str) -> None: + assert str(column) == expected diff --git a/tests/safeds/data/tabular/containers/_column/test_sum.py b/tests/safeds/data/tabular/containers/_column/test_sum.py index 74ebc6ee7..c4401f96e 100644 --- a/tests/safeds/data/tabular/containers/_column/test_sum.py +++ b/tests/safeds/data/tabular/containers/_column/test_sum.py @@ -1,14 +1,40 @@ import pytest from safeds.data.tabular.containers import Column -from safeds.data.tabular.exceptions import NonNumericColumnError +from safeds.exceptions import NonNumericColumnError -def test_sum_valid() -> None: - c1 = Column("test", [1, 2]) - assert c1.sum() == 3 +@pytest.mark.parametrize( + ("values", "expected"), + [ + ([1, 2, 3], 6), + ([1, 2, 3, 4], 10), + ([1, 2, 3, None], 6), + ], + ids=[ + "odd number of values", + "even number of values", + "some missing values", + ], +) +def test_should_return_sum_of_values(values: list, expected: int) -> None: + column = Column("A", values) + assert column.sum() == expected -def test_sum_invalid() -> None: - c1 = Column("test", [1, "a"]) +@pytest.mark.parametrize( + "values", + [ + [], + ["a", "b", "c"], + [None, None, None], + ], + ids=[ + "empty", + "non-numeric", + "all missing values", + ], +) +def test_should_raise_if_column_is_not_numeric(values: list) -> None: + column = Column("A", values) with pytest.raises(NonNumericColumnError): - c1.sum() + column.sum() diff --git a/tests/safeds/data/tabular/containers/_column/test_to_html.py b/tests/safeds/data/tabular/containers/_column/test_to_html.py new file mode 100644 index 000000000..a46574573 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_column/test_to_html.py @@ -0,0 +1,50 @@ +import pytest +import regex as re +from safeds.data.tabular.containers import Column + + +@pytest.mark.parametrize( + "column", + [ + Column("a", []), + Column("a", [1, 2, 3]), + ], + ids=[ + "empty", + "non-empty", + ], +) +def test_should_contain_table_element(column: Column) -> None: + pattern = r".*?" + assert re.search(pattern, column.to_html(), flags=re.S) is not None + + +@pytest.mark.parametrize( + "column", + [ + Column("a", []), + Column("a", [1, 2, 3]), + ], + ids=[ + "empty", + "non-empty", + ], +) +def test_should_contain_th_element_for_column_name(column: Column) -> None: + assert f"{column.name}" in column.to_html() + + +@pytest.mark.parametrize( + "column", + [ + Column("a", []), + Column("a", [1, 2, 3]), + ], + ids=[ + "empty", + "non-empty", + ], +) +def test_should_contain_td_element_for_each_value(column: Column) -> None: + for value in column: + assert f"{value}" in column.to_html() diff --git a/tests/safeds/data/tabular/containers/_column/test_transform.py b/tests/safeds/data/tabular/containers/_column/test_transform.py new file mode 100644 index 000000000..8e35ab2c0 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_column/test_transform.py @@ -0,0 +1,29 @@ +import pytest +from safeds.data.tabular.containers import Column + + +@pytest.mark.parametrize( + ("column", "expected"), + [ + (Column("test", []), Column("test", [])), + (Column("test", [1, 2]), Column("test", [2, 3])), + (Column("test", [-0.5, 0, 4]), Column("test", [0.5, 1, 5])), + ], + ids=["empty", "integers", "floats"], +) +def test_should_transform_column(column: Column, expected: Column) -> None: + assert column.transform(lambda it: it + 1) == expected + + +@pytest.mark.parametrize( + ("column", "original"), + [ + (Column("test", []), Column("test", [])), + (Column("test", [1, 2]), Column("test", [1, 2])), + (Column("test", [-0.5, 0, 4]), Column("test", [-0.5, 0, 4])), + ], + ids=["empty", "integers", "floats"], +) +def test_should_not_change_original_column(column: Column, original: Column) -> None: + column.transform(lambda it: it + 1) + assert column == original diff --git a/tests/safeds/data/tabular/containers/_column/test_variance.py b/tests/safeds/data/tabular/containers/_column/test_variance.py new file mode 100644 index 000000000..9bddbe400 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_column/test_variance.py @@ -0,0 +1,38 @@ +import pytest +from safeds.data.tabular.containers import Column +from safeds.exceptions import NonNumericColumnError + + +@pytest.mark.parametrize( + ("values", "expected"), + [ + ([1, 2, 3], 1), + ([1, 2, 3, None], 1), + ], + ids=[ + "no missing values", + "some missing values", + ], +) +def test_should_return_variance(values: list, expected: int) -> None: + column = Column("A", values) + assert column.variance() == expected + + +@pytest.mark.parametrize( + "values", + [ + [], + ["a", "b", "c"], + [None, None, None], + ], + ids=[ + "empty", + "non-numeric", + "all missing values", + ], +) +def test_should_raise_if_column_is_not_numeric(values: list) -> None: + column = Column("A", values) + with pytest.raises(NonNumericColumnError): + column.variance() diff --git a/tests/safeds/ml/classification/__init__.py b/tests/safeds/data/tabular/containers/_table/_tagged_table/__init__.py similarity index 100% rename from tests/safeds/ml/classification/__init__.py rename to tests/safeds/data/tabular/containers/_table/_tagged_table/__init__.py diff --git a/tests/safeds/data/tabular/containers/_table/_tagged_table/test_add_column.py b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_add_column.py new file mode 100644 index 000000000..22bd87605 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_add_column.py @@ -0,0 +1,34 @@ +import pytest +from safeds.data.tabular.containers import Column, TaggedTable + +from tests.helpers import assert_that_tagged_tables_are_equal + + +@pytest.mark.parametrize( + ("tagged_table", "column", "expected_tagged_table"), + [ + ( + TaggedTable( + { + "feature_1": [0, 1, 2], + "target": [3, 4, 5], + }, + "target", + None, + ), + Column("other", [6, 7, 8]), + TaggedTable( + { + "feature_1": [0, 1, 2], + "target": [3, 4, 5], + "other": [6, 7, 8], + }, + "target", + ["feature_1"], + ), + ), + ], + ids=["add_column_as_non_feature"], +) +def test_should_add_column(tagged_table: TaggedTable, column: Column, expected_tagged_table: TaggedTable) -> None: + assert_that_tagged_tables_are_equal(tagged_table.add_column(column), expected_tagged_table) diff --git a/tests/safeds/data/tabular/containers/_table/_tagged_table/test_add_column_as_feature.py b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_add_column_as_feature.py new file mode 100644 index 000000000..325df54db --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_add_column_as_feature.py @@ -0,0 +1,37 @@ +import pytest +from safeds.data.tabular.containers import Column, Table, TaggedTable + +from tests.helpers import assert_that_tagged_tables_are_equal + + +@pytest.mark.parametrize( + ("tagged_table", "column", "tagged_table_with_new_column"), + [ + ( + Table({"f1": [1, 2], "target": [2, 3]}).tag_columns(target_name="target", feature_names=["f1"]), + Column("f2", [4, 5]), + Table({"f1": [1, 2], "target": [2, 3], "f2": [4, 5]}).tag_columns( + target_name="target", + feature_names=["f1", "f2"], + ), + ), + ( + Table({"f1": [1, 2], "target": [2, 3], "other": [0, -1]}).tag_columns( + target_name="target", + feature_names=["f1"], + ), + Column("f2", [4, 5]), + Table({"f1": [1, 2], "target": [2, 3], "other": [0, -1], "f2": [4, 5]}).tag_columns( + target_name="target", + feature_names=["f1", "f2"], + ), + ), + ], + ids=["new column as feature", "table contains a non feature/target column"], +) +def test_add_column_as_feature( + tagged_table: TaggedTable, + column: Column, + tagged_table_with_new_column: TaggedTable, +) -> None: + assert_that_tagged_tables_are_equal(tagged_table.add_column_as_feature(column), tagged_table_with_new_column) diff --git a/tests/safeds/data/tabular/containers/_table/_tagged_table/test_add_columns.py b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_add_columns.py new file mode 100644 index 000000000..8773e3695 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_add_columns.py @@ -0,0 +1,39 @@ +import pytest +from safeds.data.tabular.containers import Column, TaggedTable + +from tests.helpers import assert_that_tagged_tables_are_equal + + +@pytest.mark.parametrize( + ("tagged_table", "columns", "expected_tagged_table"), + [ + ( + TaggedTable( + { + "feature_1": [0, 1, 2], + "target": [3, 4, 5], + }, + "target", + None, + ), + [Column("other", [6, 7, 8]), Column("other2", [9, 6, 3])], + TaggedTable( + { + "feature_1": [0, 1, 2], + "target": [3, 4, 5], + "other": [6, 7, 8], + "other2": [9, 6, 3], + }, + "target", + ["feature_1"], + ), + ), + ], + ids=["add_columns_as_non_feature"], +) +def test_should_add_columns( + tagged_table: TaggedTable, + columns: list[Column], + expected_tagged_table: TaggedTable, +) -> None: + assert_that_tagged_tables_are_equal(tagged_table.add_columns(columns), expected_tagged_table) diff --git a/tests/safeds/data/tabular/containers/_table/_tagged_table/test_add_columns_as_features.py b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_add_columns_as_features.py new file mode 100644 index 000000000..f1e7716b8 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_add_columns_as_features.py @@ -0,0 +1,45 @@ +import pytest +from safeds.data.tabular.containers import Column, Table, TaggedTable + +from tests.helpers import assert_that_tagged_tables_are_equal + + +@pytest.mark.parametrize( + ("tagged_table", "columns", "tagged_table_with_new_columns"), + [ + ( + Table({"f1": [1, 2], "target": [2, 3]}).tag_columns(target_name="target", feature_names=["f1"]), + [Column("f2", [4, 5]), Column("f3", [6, 7])], + Table({"f1": [1, 2], "target": [2, 3], "f2": [4, 5], "f3": [6, 7]}).tag_columns( + target_name="target", + feature_names=["f1", "f2", "f3"], + ), + ), + ( + Table({"f1": [1, 2], "target": [2, 3]}).tag_columns(target_name="target", feature_names=["f1"]), + Table.from_columns([Column("f2", [4, 5]), Column("f3", [6, 7])]), + Table({"f1": [1, 2], "target": [2, 3], "f2": [4, 5], "f3": [6, 7]}).tag_columns( + target_name="target", + feature_names=["f1", "f2", "f3"], + ), + ), + ( + Table({"f1": [1, 2], "target": [2, 3], "other": [0, -1]}).tag_columns( + target_name="target", + feature_names=["f1"], + ), + Table.from_columns([Column("f2", [4, 5]), Column("f3", [6, 7])]), + Table({"f1": [1, 2], "target": [2, 3], "other": [0, -1], "f2": [4, 5], "f3": [6, 7]}).tag_columns( + target_name="target", + feature_names=["f1", "f2", "f3"], + ), + ), + ], + ids=["new columns as feature", "table added as features", "table contains a non feature/target column"], +) +def test_add_columns_as_features( + tagged_table: TaggedTable, + columns: list[Column] | Table, + tagged_table_with_new_columns: TaggedTable, +) -> None: + assert_that_tagged_tables_are_equal(tagged_table.add_columns_as_features(columns), tagged_table_with_new_columns) diff --git a/tests/safeds/data/tabular/containers/_table/_tagged_table/test_add_row.py b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_add_row.py new file mode 100644 index 000000000..2badeec11 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_add_row.py @@ -0,0 +1,36 @@ +import pytest +from safeds.data.tabular.containers import Row, TaggedTable + +from tests.helpers import assert_that_tagged_tables_are_equal + + +@pytest.mark.parametrize( + ("table", "row", "expected"), + [ + ( + TaggedTable( + { + "feature": [0, 1], + "target": [3, 4], + }, + "target", + ), + Row( + { + "feature": 2, + "target": 5, + }, + ), + TaggedTable( + { + "feature": [0, 1, 2], + "target": [3, 4, 5], + }, + "target", + ), + ), + ], + ids=["add_row"], +) +def test_should_add_row(table: TaggedTable, row: Row, expected: TaggedTable) -> None: + assert_that_tagged_tables_are_equal(table.add_row(row), expected) diff --git a/tests/safeds/data/tabular/containers/_table/_tagged_table/test_add_rows.py b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_add_rows.py new file mode 100644 index 000000000..da8c37a5a --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_add_rows.py @@ -0,0 +1,39 @@ +import pytest +from safeds.data.tabular.containers import Row, TaggedTable + +from tests.helpers import assert_that_tagged_tables_are_equal + + +@pytest.mark.parametrize( + ("table", "rows", "expected"), + [ + ( + TaggedTable( + { + "feature": [0, 1], + "target": [4, 5], + }, + "target", + ), + [ + Row( + { + "feature": 2, + "target": 6, + }, + ), + Row({"feature": 3, "target": 7}), + ], + TaggedTable( + { + "feature": [0, 1, 2, 3], + "target": [4, 5, 6, 7], + }, + "target", + ), + ), + ], + ids=["add_rows"], +) +def test_should_add_rows(table: TaggedTable, rows: list[Row], expected: TaggedTable) -> None: + assert_that_tagged_tables_are_equal(table.add_rows(rows), expected) diff --git a/tests/safeds/data/tabular/containers/_table/_tagged_table/test_as_table.py b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_as_table.py new file mode 100644 index 000000000..6c9480671 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_as_table.py @@ -0,0 +1,52 @@ +import pytest +from safeds.data.tabular.containers import Table, TaggedTable + + +@pytest.mark.parametrize( + ("tagged_table", "expected"), + [ + ( + TaggedTable( + { + "feature_1": [3, 9, 6], + "feature_2": [6, 12, 9], + "target": [1, 3, 2], + }, + "target", + ["feature_1", "feature_2"], + ), + Table( + { + "feature_1": [3, 9, 6], + "feature_2": [6, 12, 9], + "target": [1, 3, 2], + }, + ), + ), + ( + TaggedTable( + { + "feature_1": [3, 9, 6], + "feature_2": [6, 12, 9], + "other": [3, 9, 12], + "target": [1, 3, 2], + }, + "target", + ["feature_1", "feature_2"], + ), + Table( + { + "feature_1": [3, 9, 6], + "feature_2": [6, 12, 9], + "other": [3, 9, 12], + "target": [1, 3, 2], + }, + ), + ), + ], + ids=["normal", "table_with_column_as_non_feature"], +) +def test_should_return_table(tagged_table: TaggedTable, expected: Table) -> None: + table = tagged_table._as_table() + assert table.schema == expected.schema + assert table == expected diff --git a/tests/safeds/data/tabular/containers/_table/_tagged_table/test_copy.py b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_copy.py new file mode 100644 index 000000000..8819aff05 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_copy.py @@ -0,0 +1,22 @@ +import pytest +from safeds.data.tabular.containers import TaggedTable + + +@pytest.mark.parametrize( + "tagged_table", + [ + TaggedTable({"a": [], "b": []}, target_name="b", feature_names=["a"]), + TaggedTable({"a": ["a", 3, 0.1], "b": [True, False, None]}, target_name="b", feature_names=["a"]), + TaggedTable( + {"a": ["a", 3, 0.1], "b": [True, False, None], "c": ["a", "b", "c"]}, + target_name="b", + feature_names=["a"], + ), + TaggedTable({"a": [], "b": [], "c": []}, target_name="b", feature_names=["a"]), + ], + ids=["empty-rows", "normal", "column_as_non_feature", "column_as_non_feature_with_empty_rows"], +) +def test_should_copy_tagged_table(tagged_table: TaggedTable) -> None: + copied = tagged_table._copy() + assert copied == tagged_table + assert copied is not tagged_table diff --git a/tests/safeds/data/tabular/containers/_table/_tagged_table/test_features.py b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_features.py new file mode 100644 index 000000000..2af13d37c --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_features.py @@ -0,0 +1,37 @@ +import pytest +from safeds.data.tabular.containers import Table, TaggedTable + + +@pytest.mark.parametrize( + ("tagged_table", "features"), + [ + ( + TaggedTable( + { + "A": [1, 4], + "B": [2, 5], + "C": [3, 6], + "T": [0, 1], + }, + target_name="T", + ), + Table({"A": [1, 4], "B": [2, 5], "C": [3, 6]}), + ), + ( + TaggedTable( + { + "A": [1, 4], + "B": [2, 5], + "C": [3, 6], + "T": [0, 1], + }, + target_name="T", + feature_names=["A", "C"], + ), + Table({"A": [1, 4], "C": [3, 6]}), + ), + ], + ids=["only_target_and_features", "target_features_and_other"], +) +def test_should_return_features(tagged_table: TaggedTable, features: Table) -> None: + assert tagged_table.features == features diff --git a/tests/safeds/data/tabular/containers/_table/_tagged_table/test_filter_rows.py b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_filter_rows.py new file mode 100644 index 000000000..98ba1bba6 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_filter_rows.py @@ -0,0 +1,105 @@ +from collections.abc import Callable + +import pytest +from safeds.data.tabular.containers import Row, TaggedTable + +from tests.helpers import assert_that_tagged_tables_are_equal + + +@pytest.mark.parametrize( + ("table", "expected", "query"), + [ + ( + TaggedTable( + { + "feature_1": [3, 9, 6], + "feature_2": [6, 12, 9], + "target": [1, 3, 2], + }, + "target", + ), + TaggedTable( + { + "feature_1": [3, 6], + "feature_2": [6, 9], + "target": [1, 2], + }, + "target", + ), + lambda row: all(row.get_value(col) < 10 for col in row.column_names), + ), + ( + TaggedTable( + { + "feature_1": [3, 9, 6, 2], + "feature_2": [6, 12, 9, 3], + "other": [1, 2, 3, 10], + "target": [1, 3, 2, 4], + }, + "target", + ["feature_1", "feature_2"], + ), + TaggedTable( + { + "feature_1": [3, 6], + "feature_2": [6, 9], + "other": [1, 3], + "target": [1, 2], + }, + "target", + ["feature_1", "feature_2"], + ), + lambda row: all(row.get_value(col) < 10 for col in row.column_names), + ), + ( + TaggedTable( + { + "feature_1": [3, 9, 6], + "feature_2": [6, 12, 9], + "target": [1, 3, 2], + }, + "target", + ), + TaggedTable( + { + "feature_1": [3, 9, 6], + "feature_2": [6, 12, 9], + "target": [1, 3, 2], + }, + "target", + ), + lambda row: all(row.get_value(col) < 20 for col in row.column_names), + ), + ( + TaggedTable( + { + "feature_1": [3, 9, 6, 2], + "feature_2": [6, 12, 9, 3], + "other": [1, 2, 3, 10], + "target": [1, 3, 2, 4], + }, + "target", + ["feature_1", "feature_2"], + ), + TaggedTable( + { + "feature_1": [3, 9, 6, 2], + "feature_2": [6, 12, 9, 3], + "other": [1, 2, 3, 10], + "target": [1, 3, 2, 4], + }, + "target", + ["feature_1", "feature_2"], + ), + lambda row: all(row.get_value(col) < 20 for col in row.column_names), + ), + ], + ids=[ + "remove_rows_with_values_greater_9", + "remove_rows_with_values_greater_9_non_feature_columns", + "remove_no_rows", + "remove_no_rows_non_feature_columns", + ], +) +def test_should_filter_rows(table: TaggedTable, expected: TaggedTable, query: Callable[[Row], bool]) -> None: + assert_that_tagged_tables_are_equal(table.filter_rows(query), expected) diff --git a/tests/safeds/data/tabular/containers/_table/_tagged_table/test_from_table.py b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_from_table.py new file mode 100644 index 000000000..fc99ff58d --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_from_table.py @@ -0,0 +1,145 @@ +import pytest +from safeds.data.tabular.containers import Table, TaggedTable +from safeds.exceptions import UnknownColumnNameError + + +@pytest.mark.parametrize( + ("table", "target_name", "feature_names", "error", "error_msg"), + [ + ( + Table( + { + "A": [1, 4], + "B": [2, 5], + "C": [3, 6], + "T": [0, 1], + }, + ), + "T", + ["A", "B", "C", "D", "E"], + UnknownColumnNameError, + r"Could not find column\(s\) 'D, E'", + ), + ( + Table( + { + "A": [1, 4], + "B": [2, 5], + "C": [3, 6], + "T": [0, 1], + }, + ), + "D", + ["A", "B", "C"], + UnknownColumnNameError, + r"Could not find column\(s\) 'D'", + ), + ( + Table( + { + "A": [1, 4], + "B": [2, 5], + "C": [3, 6], + "T": [0, 1], + }, + ), + "A", + ["A", "B", "C"], + ValueError, + r"Column 'A' cannot be both feature and target.", + ), + ( + Table( + { + "A": [1, 4], + "B": [2, 5], + "C": [3, 6], + "T": [0, 1], + }, + ), + "A", + [], + ValueError, + r"At least one feature column must be specified.", + ), + ( + Table( + { + "A": [1, 4], + }, + ), + "A", + None, + ValueError, + r"At least one feature column must be specified.", + ), + ], + ids=[ + "feature_does_not_exist", + "target_does_not_exist", + "target_and_feature_overlap", + "features_are_empty-explicitly", + "features_are_empty_implicitly", + ], +) +def test_should_raise_error( + table: Table, + target_name: str, + feature_names: list[str] | None, + error: type[Exception], + error_msg: str, +) -> None: + with pytest.raises(error, match=error_msg): + TaggedTable._from_table(table, target_name=target_name, feature_names=feature_names) + + +@pytest.mark.parametrize( + ("table", "target_name", "feature_names"), + [ + ( + Table( + { + "A": [1, 4], + "B": [2, 5], + "C": [3, 6], + "T": [0, 1], + }, + ), + "T", + ["A", "B", "C"], + ), + ( + Table( + { + "A": [1, 4], + "B": [2, 5], + "C": [3, 6], + "T": [0, 1], + }, + ), + "T", + ["A", "C"], + ), + ( + Table( + { + "A": [1, 4], + "B": [2, 5], + "C": [3, 6], + "T": [0, 1], + }, + ), + "T", + None, + ), + ], + ids=["create_tagged_table", "tagged_table_not_all_columns_are_features", "tagged_table_with_feature_names_as_None"], +) +def test_should_create_a_tagged_table(table: Table, target_name: str, feature_names: list[str] | None) -> None: + tagged_table = TaggedTable._from_table(table, target_name=target_name, feature_names=feature_names) + feature_names = feature_names if feature_names is not None else table.remove_columns([target_name]).column_names + assert isinstance(tagged_table, TaggedTable) + assert tagged_table._features.column_names == feature_names + assert tagged_table._target.name == target_name + assert tagged_table._features == table.keep_only_columns(feature_names) + assert tagged_table._target == table.get_column(target_name) diff --git a/tests/safeds/data/tabular/containers/_table/_tagged_table/test_init.py b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_init.py new file mode 100644 index 000000000..723dfc990 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_init.py @@ -0,0 +1,135 @@ +import pytest +from safeds.data.tabular.containers import Table, TaggedTable +from safeds.exceptions import UnknownColumnNameError + + +@pytest.mark.parametrize( + ("data", "target_name", "feature_names", "error", "error_msg"), + [ + ( + { + "A": [1, 4], + "B": [2, 5], + "C": [3, 6], + "T": [0, 1], + }, + "T", + ["A", "B", "C", "D", "E"], + UnknownColumnNameError, + r"Could not find column\(s\) 'D, E'", + ), + ( + { + "A": [1, 4], + "B": [2, 5], + "C": [3, 6], + "T": [0, 1], + }, + "D", + ["A", "B", "C"], + UnknownColumnNameError, + r"Could not find column\(s\) 'D'", + ), + ( + { + "A": [1, 4], + "B": [2, 5], + "C": [3, 6], + "T": [0, 1], + }, + "A", + ["A", "B", "C"], + ValueError, + r"Column 'A' cannot be both feature and target.", + ), + ( + { + "A": [1, 4], + "B": [2, 5], + "C": [3, 6], + "T": [0, 1], + }, + "D", + [], + ValueError, + r"At least one feature column must be specified.", + ), + ( + { + "A": [1, 4], + }, + "A", + None, + ValueError, + r"At least one feature column must be specified.", + ), + ], + ids=[ + "feature_does_not_exist", + "target_does_not_exist", + "target_and_feature_overlap", + "features_are_empty-explicitly", + "features_are_empty_implicitly", + ], +) +def test_should_raise_error( + data: dict[str, list[int]], + target_name: str, + feature_names: list[str] | None, + error: type[Exception], + error_msg: str, +) -> None: + with pytest.raises(error, match=error_msg): + TaggedTable(data, target_name=target_name, feature_names=feature_names) + + +@pytest.mark.parametrize( + ("data", "target_name", "feature_names"), + [ + ( + { + "A": [1, 4], + "B": [2, 5], + "C": [3, 6], + "T": [0, 1], + }, + "T", + ["A", "B", "C"], + ), + ( + { + "A": [1, 4], + "B": [2, 5], + "C": [3, 6], + "T": [0, 1], + }, + "T", + ["A", "C"], + ), + ( + { + "A": [1, 4], + "B": [2, 5], + "C": [3, 6], + "T": [0, 1], + }, + "T", + None, + ), + ], + ids=["create_tagged_table", "tagged_table_not_all_columns_are_features", "tagged_table_with_feature_names_as_None"], +) +def test_should_create_a_tagged_table( + data: dict[str, list[int]], + target_name: str, + feature_names: list[str] | None, +) -> None: + tagged_table = TaggedTable(data, target_name=target_name, feature_names=feature_names) + if feature_names is None: + feature_names = list(data.keys()) + feature_names.remove(target_name) + assert isinstance(tagged_table, TaggedTable) + assert tagged_table._features.column_names == feature_names + assert tagged_table._target.name == target_name + assert tagged_table._features == Table(data).keep_only_columns(feature_names) + assert tagged_table._target == Table(data).get_column(target_name) diff --git a/tests/safeds/data/tabular/containers/_table/_tagged_table/test_keep_only_columns.py b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_keep_only_columns.py new file mode 100644 index 000000000..975dca9e9 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_keep_only_columns.py @@ -0,0 +1,131 @@ +import pytest +from safeds.data.tabular.containers import Table, TaggedTable +from safeds.exceptions import IllegalSchemaModificationError + +from tests.helpers import assert_that_tagged_tables_are_equal + + +@pytest.mark.parametrize( + ("table", "column_names", "expected"), + [ + ( + TaggedTable._from_table( + Table( + { + "feat1": [1, 2, 3], + "feat2": [4, 5, 6], + "target": [7, 8, 9], + }, + ), + "target", + ), + ["feat1", "target"], + TaggedTable._from_table( + Table( + { + "feat1": [1, 2, 3], + "target": [7, 8, 9], + }, + ), + "target", + ), + ), + ( + TaggedTable._from_table( + Table( + { + "feat1": [1, 2, 3], + "feat2": [4, 5, 6], + "other": [3, 4, 5], + "target": [7, 8, 9], + }, + ), + "target", + ), + ["feat1", "other", "target"], + TaggedTable._from_table( + Table( + { + "feat1": [1, 2, 3], + "other": [3, 4, 5], + "target": [7, 8, 9], + }, + ), + "target", + ), + ), + ( + TaggedTable._from_table( + Table( + { + "feat1": [1, 2, 3], + "feat2": [4, 5, 6], + "other": [3, 4, 5], + "target": [7, 8, 9], + }, + ), + "target", + ), + ["feat1", "target"], + TaggedTable._from_table( + Table( + { + "feat1": [1, 2, 3], + "target": [7, 8, 9], + }, + ), + "target", + ), + ), + ], + ids=["keep_feature_and_target_column", "keep_non_feature_column", "don't_keep_non_feature_column"], +) +def test_should_return_table(table: TaggedTable, column_names: list[str], expected: TaggedTable) -> None: + new_table = table.keep_only_columns(column_names) + assert_that_tagged_tables_are_equal(new_table, expected) + + +@pytest.mark.parametrize( + ("table", "column_names", "error_msg"), + [ + ( + TaggedTable._from_table( + Table( + { + "feat1": [1, 2, 3], + "feat2": [4, 5, 6], + "other": [3, 5, 7], + "target": [7, 8, 9], + }, + ), + "target", + ["feat1", "feat2"], + ), + ["feat1", "feat2"], + r"Illegal schema modification: Must keep the target column.", + ), + ( + TaggedTable._from_table( + Table( + { + "feat1": [1, 2, 3], + "feat2": [4, 5, 6], + "other": [3, 5, 7], + "target": [7, 8, 9], + }, + ), + "target", + ["feat1", "feat2"], + ), + ["target", "other"], + r"Illegal schema modification: Must keep at least one feature column.", + ), + ], + ids=["table_remove_target", "table_remove_all_features"], +) +def test_should_raise_illegal_schema_modification(table: TaggedTable, column_names: list[str], error_msg: str) -> None: + with pytest.raises( + IllegalSchemaModificationError, + match=error_msg, + ): + table.keep_only_columns(column_names) diff --git a/tests/safeds/data/tabular/containers/_table/_tagged_table/test_remove_columns.py b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_remove_columns.py new file mode 100644 index 000000000..9e8435885 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_remove_columns.py @@ -0,0 +1,181 @@ +import pytest +from safeds.data.tabular.containers import Table, TaggedTable +from safeds.exceptions import ColumnIsTargetError, IllegalSchemaModificationError + +from tests.helpers import assert_that_tagged_tables_are_equal + + +@pytest.mark.parametrize( + ("table", "columns", "expected"), + [ + ( + TaggedTable._from_table( + Table( + { + "feat_1": [1, 2, 3], + "feat_2": [4, 5, 6], + "non_feat_1": [2, 4, 6], + "non_feat_2": [3, 6, 9], + "target": [7, 8, 9], + }, + ), + "target", + ["feat_1", "feat_2"], + ), + ["feat_2"], + TaggedTable._from_table( + Table({"feat_1": [1, 2, 3], "non_feat_1": [2, 4, 6], "non_feat_2": [3, 6, 9], "target": [7, 8, 9]}), + "target", + ["feat_1"], + ), + ), + ( + TaggedTable._from_table( + Table( + { + "feat_1": [1, 2, 3], + "feat_2": [4, 5, 6], + "non_feat_1": [2, 4, 6], + "non_feat_2": [3, 6, 9], + "target": [7, 8, 9], + }, + ), + "target", + ["feat_1", "feat_2"], + ), + ["non_feat_2"], + TaggedTable._from_table( + Table({"feat_1": [1, 2, 3], "feat_2": [4, 5, 6], "non_feat_1": [2, 4, 6], "target": [7, 8, 9]}), + "target", + ["feat_1", "feat_2"], + ), + ), + ( + TaggedTable._from_table( + Table( + { + "feat_1": [1, 2, 3], + "feat_2": [4, 5, 6], + "non_feat_1": [2, 4, 6], + "non_feat_2": [3, 6, 9], + "target": [7, 8, 9], + }, + ), + "target", + ["feat_1", "feat_2"], + ), + ["non_feat_1", "non_feat_2"], + TaggedTable._from_table( + Table({"feat_1": [1, 2, 3], "feat_2": [4, 5, 6], "target": [7, 8, 9]}), + "target", + ["feat_1", "feat_2"], + ), + ), + ( + TaggedTable._from_table( + Table( + { + "feat_1": [1, 2, 3], + "feat_2": [4, 5, 6], + "non_feat_1": [2, 4, 6], + "non_feat_2": [3, 6, 9], + "target": [7, 8, 9], + }, + ), + "target", + ["feat_1", "feat_2"], + ), + ["feat_2", "non_feat_2"], + TaggedTable._from_table( + Table({"feat_1": [1, 2, 3], "non_feat_1": [2, 4, 6], "target": [7, 8, 9]}), + "target", + ["feat_1"], + ), + ), + ( + TaggedTable._from_table( + Table( + { + "feat_1": [1, 2, 3], + "non_feat_1": [2, 4, 6], + "target": [7, 8, 9], + }, + ), + "target", + ["feat_1"], + ), + [], + TaggedTable._from_table( + Table({"feat_1": [1, 2, 3], "non_feat_1": [2, 4, 6], "target": [7, 8, 9]}), + "target", + ["feat_1"], + ), + ), + ], + ids=[ + "remove_feature", + "remove_non_feature", + "remove_all_non_features", + "remove_some_feat_and_some_non_feat", + "remove_nothing", + ], +) +def test_should_remove_columns(table: TaggedTable, columns: list[str], expected: TaggedTable) -> None: + new_table = table.remove_columns(columns) + assert_that_tagged_tables_are_equal(new_table, expected) + + +@pytest.mark.parametrize( + ("table", "columns", "error", "error_msg"), + [ + ( + TaggedTable._from_table( + Table({"feat": [1, 2, 3], "non_feat": [1, 2, 3], "target": [4, 5, 6]}), + "target", + ["feat"], + ), + ["target"], + ColumnIsTargetError, + r'Illegal schema modification: Column "target" is the target column and cannot be removed.', + ), + ( + TaggedTable._from_table( + Table({"feat": [1, 2, 3], "non_feat": [1, 2, 3], "target": [4, 5, 6]}), + "target", + ["feat"], + ), + ["non_feat", "target"], + ColumnIsTargetError, + r'Illegal schema modification: Column "target" is the target column and cannot be removed.', + ), + ( + TaggedTable._from_table( + Table({"feat": [1, 2, 3], "non_feat": [1, 2, 3], "target": [4, 5, 6]}), + "target", + ["feat"], + ), + ["feat"], + IllegalSchemaModificationError, + r"Illegal schema modification: You cannot remove every feature column.", + ), + ( + TaggedTable._from_table( + Table({"feat": [1, 2, 3], "non_feat": [1, 2, 3], "target": [4, 5, 6]}), + "target", + ["feat"], + ), + ["feat", "non_feat"], + IllegalSchemaModificationError, + r"Illegal schema modification: You cannot remove every feature column.", + ), + ], + ids=["remove_only_target", "remove_non_feat_and_target", "remove_all_features", "remove_non_feat_and_all_features"], +) +def test_should_raise_in_remove_columns( + table: TaggedTable, + columns: list[str], + error: type[Exception], + error_msg: str, +) -> None: + with pytest.raises(error, match=error_msg): + table.remove_columns(columns) diff --git a/tests/safeds/data/tabular/containers/_table/_tagged_table/test_remove_columns_with_missing_values.py b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_remove_columns_with_missing_values.py new file mode 100644 index 000000000..b442fe0d0 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_remove_columns_with_missing_values.py @@ -0,0 +1,181 @@ +import pytest +from safeds.data.tabular.containers import TaggedTable +from safeds.exceptions import ColumnIsTargetError, IllegalSchemaModificationError + +from tests.helpers import assert_that_tagged_tables_are_equal + + +@pytest.mark.parametrize( + ("table", "expected"), + [ + ( + TaggedTable( + { + "feature_complete": [0, 1, 2], + "feature_incomplete": [3, None, 5], + "non_feature_complete": [7, 8, 9], + "target": [3, 4, 5], + }, + "target", + ["feature_complete", "feature_incomplete"], + ), + TaggedTable( + { + "feature_complete": [0, 1, 2], + "non_feature_complete": [7, 8, 9], + "target": [3, 4, 5], + }, + "target", + ["feature_complete"], + ), + ), + ( + TaggedTable( + { + "feature_complete": [0, 1, 2], + "non_feature_complete": [7, 8, 9], + "non_feature_incomplete": [3, None, 5], + "target": [3, 4, 5], + }, + "target", + ["feature_complete"], + ), + TaggedTable( + { + "feature_complete": [0, 1, 2], + "non_feature_complete": [7, 8, 9], + "target": [3, 4, 5], + }, + "target", + ["feature_complete"], + ), + ), + ( + TaggedTable( + { + "feature_complete": [0, 1, 2], + "non_feature_complete": [7, 8, 9], + "target": [3, 4, 5], + }, + "target", + ["feature_complete"], + ), + TaggedTable( + { + "feature_complete": [0, 1, 2], + "non_feature_complete": [7, 8, 9], + "target": [3, 4, 5], + }, + "target", + ["feature_complete"], + ), + ), + ], + ids=["incomplete_feature", "incomplete_non_feature", "all_complete"], +) +def test_should_remove_columns_with_non_numerical_values(table: TaggedTable, expected: TaggedTable) -> None: + new_table = table.remove_columns_with_missing_values() + assert_that_tagged_tables_are_equal(new_table, expected) + + +@pytest.mark.parametrize( + ("table", "error", "error_msg"), + [ + ( + TaggedTable( + { + "feature": [0, 1, 2], + "non_feature": [1, 2, 3], + "target": [3, None, 5], + }, + "target", + ["feature"], + ), + ColumnIsTargetError, + 'Illegal schema modification: Column "target" is the target column and cannot be removed.', + ), + ( + TaggedTable( + { + "feature": [0, None, 2], + "non_feature": [1, 2, 3], + "target": [None, 4, 5], + }, + "target", + ["feature"], + ), + ColumnIsTargetError, + 'Illegal schema modification: Column "target" is the target column and cannot be removed.', + ), + ( + TaggedTable( + { + "feature": [0, 1, 2], + "non_feature": [1, None, 3], + "target": [3, 4, None], + }, + "target", + ["feature"], + ), + ColumnIsTargetError, + 'Illegal schema modification: Column "target" is the target column and cannot be removed.', + ), + ( + TaggedTable( + { + "feature": [0, None, 2], + "non_feature": [1, None, 3], + "target": [3, None, 5], + }, + "target", + ["feature"], + ), + ColumnIsTargetError, + 'Illegal schema modification: Column "target" is the target column and cannot be removed.', + ), + ( + TaggedTable( + { + "feature": [0, None, 2], + "non_feature": [1, 2, 3], + "target": [3, 2, 5], + }, + "target", + ["feature"], + ), + IllegalSchemaModificationError, + "Illegal schema modification: You cannot remove every feature column.", + ), + ( + TaggedTable( + { + "feature": [0, None, 2], + "non_feature": [1, None, 3], + "target": [3, 2, 5], + }, + "target", + ["feature"], + ), + IllegalSchemaModificationError, + "Illegal schema modification: You cannot remove every feature column.", + ), + ], + ids=[ + "only_target_incomplete", + "also_feature_incomplete", + "also_non_feature_incomplete", + "all_incomplete", + "all_features_incomplete", + "all_features_and_non_feature_incomplete", + ], +) +def test_should_raise_in_remove_columns_with_missing_values( + table: TaggedTable, + error: type[Exception], + error_msg: str, +) -> None: + with pytest.raises( + error, + match=error_msg, + ): + table.remove_columns_with_missing_values() diff --git a/tests/safeds/data/tabular/containers/_table/_tagged_table/test_remove_columns_with_non_numerical_values.py b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_remove_columns_with_non_numerical_values.py new file mode 100644 index 000000000..5a6251d20 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_remove_columns_with_non_numerical_values.py @@ -0,0 +1,178 @@ +import pytest +from safeds.data.tabular.containers import TaggedTable +from safeds.exceptions import ColumnIsTargetError, IllegalSchemaModificationError + +from tests.helpers import assert_that_tagged_tables_are_equal + + +@pytest.mark.parametrize( + ("table", "expected"), + [ + ( + TaggedTable( + { + "feature_numerical": [0, 1, 2], + "feature_non_numerical": ["a", "b", "c"], + "non_feature_numerical": [7, 8, 9], + "target": [3, 4, 5], + }, + "target", + ["feature_numerical", "feature_non_numerical"], + ), + TaggedTable( + { + "feature_numerical": [0, 1, 2], + "non_feature_numerical": [7, 8, 9], + "target": [3, 4, 5], + }, + "target", + ["feature_numerical"], + ), + ), + ( + TaggedTable( + { + "feature_numerical": [0, 1, 2], + "non_feature_numerical": [7, 8, 9], + "non_feature_non_numerical": ["a", "b", "c"], + "target": [3, 4, 5], + }, + "target", + ["feature_numerical"], + ), + TaggedTable( + { + "feature_numerical": [0, 1, 2], + "non_feature_numerical": [7, 8, 9], + "target": [3, 4, 5], + }, + "target", + ["feature_numerical"], + ), + ), + ( + TaggedTable( + { + "feature_numerical": [0, 1, 2], + "non_feature_numerical": [7, 8, 9], + "target": [3, 4, 5], + }, + "target", + ["feature_numerical"], + ), + TaggedTable( + { + "feature_numerical": [0, 1, 2], + "non_feature_numerical": [7, 8, 9], + "target": [3, 4, 5], + }, + "target", + ["feature_numerical"], + ), + ), + ], + ids=["non_numerical_feature", "non_numerical_non_feature", "all_numerical"], +) +def test_should_remove_columns_with_non_numerical_values(table: TaggedTable, expected: TaggedTable) -> None: + new_table = table.remove_columns_with_non_numerical_values() + assert_that_tagged_tables_are_equal(new_table, expected) + + +@pytest.mark.parametrize( + ("table", "error", "error_msg"), + [ + ( + TaggedTable( + { + "feature": [0, 1, 2], + "non_feature": [1, 2, 3], + "target": ["a", "b", "c"], + }, + "target", + ["feature"], + ), + ColumnIsTargetError, + r'Illegal schema modification: Column "target" is the target column and cannot be removed.', + ), + ( + TaggedTable( + { + "feature": [0, "x", 2], + "non_feature": [1, 2, 3], + "target": ["a", "b", "c"], + }, + "target", + ["feature"], + ), + ColumnIsTargetError, + r'Illegal schema modification: Column "target" is the target column and cannot be removed.', + ), + ( + TaggedTable( + { + "feature": [0, 1, 2], + "non_feature": [1, "x", 3], + "target": ["a", "b", "c"], + }, + "target", + ["feature"], + ), + ColumnIsTargetError, + r'Illegal schema modification: Column "target" is the target column and cannot be removed.', + ), + ( + TaggedTable( + { + "feature": [0, "x", 2], + "non_feature": [1, "x", 3], + "target": ["a", "b", "c"], + }, + "target", + ["feature"], + ), + ColumnIsTargetError, + r'Illegal schema modification: Column "target" is the target column and cannot be removed.', + ), + ( + TaggedTable( + { + "feature": [0, "a", 2], + "non_feature": [1, 2, 3], + "target": [3, 2, 5], + }, + "target", + ["feature"], + ), + IllegalSchemaModificationError, + r"Illegal schema modification: You cannot remove every feature column.", + ), + ( + TaggedTable( + { + "feature": [0, "a", 2], + "non_feature": [1, "b", 3], + "target": [3, 2, 5], + }, + "target", + ["feature"], + ), + IllegalSchemaModificationError, + r"Illegal schema modification: You cannot remove every feature column.", + ), + ], + ids=[ + "only_target_non_numerical", + "also_feature_non_numerical", + "also_non_feature_non_numerical", + "all_non_numerical", + "all_features_non_numerical", + "all_features_and_non_feature_non_numerical", + ], +) +def test_should_raise_in_remove_columns_with_non_numerical_values( + table: TaggedTable, + error: type[Exception], + error_msg: str, +) -> None: + with pytest.raises(error, match=error_msg): + table.remove_columns_with_non_numerical_values() diff --git a/tests/safeds/data/tabular/containers/_table/_tagged_table/test_remove_duplicate_rows.py b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_remove_duplicate_rows.py new file mode 100644 index 000000000..1cc6936e3 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_remove_duplicate_rows.py @@ -0,0 +1,47 @@ +import pytest +from safeds.data.tabular.containers import TaggedTable + +from tests.helpers import assert_that_tagged_tables_are_equal + + +@pytest.mark.parametrize( + ("table", "expected"), + [ + ( + TaggedTable( + { + "feature": [0, 0, 1], + "target": [2, 2, 3], + }, + "target", + ), + TaggedTable( + { + "feature": [0, 1], + "target": [2, 3], + }, + "target", + ), + ), + ( + TaggedTable( + { + "feature": [0, 1, 2], + "target": [2, 2, 3], + }, + "target", + ), + TaggedTable( + { + "feature": [0, 1, 2], + "target": [2, 2, 3], + }, + "target", + ), + ), + ], + ids=["with_duplicate_rows", "without_duplicate_rows"], +) +def test_should_remove_duplicate_rows(table: TaggedTable, expected: TaggedTable) -> None: + new_table = table.remove_duplicate_rows() + assert_that_tagged_tables_are_equal(new_table, expected) diff --git a/tests/safeds/data/tabular/containers/_table/_tagged_table/test_remove_rows_with_missing_values.py b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_remove_rows_with_missing_values.py new file mode 100644 index 000000000..2f22f1489 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_remove_rows_with_missing_values.py @@ -0,0 +1,47 @@ +import pytest +from safeds.data.tabular.containers import TaggedTable + +from tests.helpers import assert_that_tagged_tables_are_equal + + +@pytest.mark.parametrize( + ("table", "expected"), + [ + ( + TaggedTable( + { + "feature": [0.0, None, 2.0], + "target": [3.0, 4.0, 5.0], + }, + "target", + ), + TaggedTable( + { + "feature": [0.0, 2.0], + "target": [3.0, 5.0], + }, + "target", + ), + ), + ( + TaggedTable( + { + "feature": [0.0, 1.0, 2.0], + "target": [3.0, 4.0, 5.0], + }, + "target", + ), + TaggedTable( + { + "feature": [0.0, 1.0, 2.0], + "target": [3.0, 4.0, 5.0], + }, + "target", + ), + ), + ], + ids=["with_missing_values", "without_missing_values"], +) +def test_should_remove_rows_with_missing_values(table: TaggedTable, expected: TaggedTable) -> None: + new_table = table.remove_rows_with_missing_values() + assert_that_tagged_tables_are_equal(new_table, expected) diff --git a/tests/safeds/data/tabular/containers/_table/_tagged_table/test_remove_rows_with_outliers.py b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_remove_rows_with_outliers.py new file mode 100644 index 000000000..59a5704f0 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_remove_rows_with_outliers.py @@ -0,0 +1,47 @@ +import pytest +from safeds.data.tabular.containers import TaggedTable + +from tests.helpers import assert_that_tagged_tables_are_equal + + +@pytest.mark.parametrize( + ("table", "expected"), + [ + ( + TaggedTable( + { + "feature": [1.0, 11.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], + "target": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], + }, + "target", + ), + TaggedTable( + { + "feature": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], + "target": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], + }, + "target", + ), + ), + ( + TaggedTable( + { + "feature": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], + "target": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], + }, + "target", + ), + TaggedTable( + { + "feature": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], + "target": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], + }, + "target", + ), + ), + ], + ids=["with_outliers", "no_outliers"], +) +def test_should_remove_rows_with_outliers(table: TaggedTable, expected: TaggedTable) -> None: + new_table = table.remove_rows_with_outliers() + assert_that_tagged_tables_are_equal(new_table, expected) diff --git a/tests/safeds/data/tabular/containers/_table/_tagged_table/test_rename_column.py b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_rename_column.py new file mode 100644 index 000000000..051c7fb90 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_rename_column.py @@ -0,0 +1,86 @@ +import pytest +from safeds.data.tabular.containers import TaggedTable + +from tests.helpers import assert_that_tagged_tables_are_equal + + +@pytest.mark.parametrize( + ("original_table", "old_column_name", "new_column_name", "result_table"), + [ + ( + TaggedTable( + { + "feature_old": [0, 1, 2], + "no_feature": [2, 3, 4], + "target": [3, 4, 5], + }, + target_name="target", + feature_names=["feature_old"], + ), + "feature_old", + "feature_new", + TaggedTable( + { + "feature_new": [0, 1, 2], + "no_feature": [2, 3, 4], + "target": [3, 4, 5], + }, + target_name="target", + feature_names=["feature_new"], + ), + ), + ( + TaggedTable( + { + "feature": [0, 1, 2], + "no_feature": [2, 3, 4], + "target_old": [3, 4, 5], + }, + target_name="target_old", + feature_names=["feature"], + ), + "target_old", + "target_new", + TaggedTable( + { + "feature": [0, 1, 2], + "no_feature": [2, 3, 4], + "target_new": [3, 4, 5], + }, + target_name="target_new", + feature_names=["feature"], + ), + ), + ( + TaggedTable( + { + "feature": [0, 1, 2], + "no_feature_old": [2, 3, 4], + "target": [3, 4, 5], + }, + target_name="target", + feature_names=["feature"], + ), + "no_feature_old", + "no_feature_new", + TaggedTable( + { + "feature": [0, 1, 2], + "no_feature_new": [2, 3, 4], + "target": [3, 4, 5], + }, + target_name="target", + feature_names=["feature"], + ), + ), + ], + ids=["rename_feature_column", "rename_target_column", "rename_non_feature_column"], +) +def test_should_add_column( + original_table: TaggedTable, + old_column_name: str, + new_column_name: str, + result_table: TaggedTable, +) -> None: + new_table = original_table.rename_column(old_column_name, new_column_name) + assert_that_tagged_tables_are_equal(new_table, result_table) diff --git a/tests/safeds/data/tabular/containers/_table/_tagged_table/test_replace_column.py b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_replace_column.py new file mode 100644 index 000000000..72b773adc --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_replace_column.py @@ -0,0 +1,179 @@ +import pytest +from safeds.data.tabular.containers import Column, TaggedTable +from safeds.exceptions import IllegalSchemaModificationError + +from tests.helpers import assert_that_tagged_tables_are_equal + + +@pytest.mark.parametrize( + ("original_table", "new_columns", "column_name_to_be_replaced", "result_table"), + [ + ( + TaggedTable( + { + "feature_old": [0, 1, 2], + "no_feature_old": [2, 3, 4], + "target_old": [3, 4, 5], + }, + "target_old", + ["feature_old"], + ), + [Column("feature_new", [2, 1, 0])], + "feature_old", + TaggedTable( + { + "feature_new": [2, 1, 0], + "no_feature_old": [2, 3, 4], + "target_old": [3, 4, 5], + }, + "target_old", + ["feature_new"], + ), + ), + ( + TaggedTable( + { + "feature_old": [0, 1, 2], + "no_feature_old": [2, 3, 4], + "target_old": [3, 4, 5], + }, + "target_old", + ["feature_old"], + ), + [Column("feature_new_a", [2, 1, 0]), Column("feature_new_b", [4, 2, 0])], + "feature_old", + TaggedTable( + { + "feature_new_a": [2, 1, 0], + "feature_new_b": [4, 2, 0], + "no_feature_old": [2, 3, 4], + "target_old": [3, 4, 5], + }, + "target_old", + ["feature_new_a", "feature_new_b"], + ), + ), + ( + TaggedTable( + { + "feature_old": [0, 1, 2], + "no_feature_old": [2, 3, 4], + "target_old": [3, 4, 5], + }, + "target_old", + ["feature_old"], + ), + [Column("no_feature_new", [2, 1, 0])], + "no_feature_old", + TaggedTable( + { + "feature_old": [0, 1, 2], + "no_feature_new": [2, 1, 0], + "target_old": [3, 4, 5], + }, + "target_old", + ["feature_old"], + ), + ), + ( + TaggedTable( + { + "feature_old": [0, 1, 2], + "no_feature_old": [2, 3, 4], + "target_old": [3, 4, 5], + }, + "target_old", + ["feature_old"], + ), + [Column("no_feature_new_a", [2, 1, 0]), Column("no_feature_new_b", [4, 2, 0])], + "no_feature_old", + TaggedTable( + { + "feature_old": [0, 1, 2], + "no_feature_new_a": [2, 1, 0], + "no_feature_new_b": [4, 2, 0], + "target_old": [3, 4, 5], + }, + "target_old", + ["feature_old"], + ), + ), + ( + TaggedTable( + { + "feature_old": [0, 1, 2], + "no_feature_old": [2, 3, 4], + "target_old": [3, 4, 5], + }, + "target_old", + ["feature_old"], + ), + [Column("target_new", [2, 1, 0])], + "target_old", + TaggedTable( + { + "feature_old": [0, 1, 2], + "no_feature_old": [2, 3, 4], + "target_new": [2, 1, 0], + }, + "target_new", + ["feature_old"], + ), + ), + ], + ids=[ + "replace_feature_column_with_one", + "replace_feature_column_with_multiple", + "replace_non_feature_column_with_one", + "replace_non_feature_column_with_multiple", + "replace_target_column", + ], +) +def test_should_replace_column( + original_table: TaggedTable, + new_columns: list[Column], + column_name_to_be_replaced: str, + result_table: TaggedTable, +) -> None: + new_table = original_table.replace_column(column_name_to_be_replaced, new_columns) + assert_that_tagged_tables_are_equal(new_table, result_table) + + +@pytest.mark.parametrize( + ("original_table", "new_columns", "column_name_to_be_replaced"), + [ + ( + TaggedTable( + { + "feature_old": [0, 1, 2], + "target_old": [3, 4, 5], + }, + "target_old", + ), + [], + "target_old", + ), + ( + TaggedTable( + { + "feature_old": [0, 1, 2], + "target_old": [3, 4, 5], + }, + "target_old", + ), + [Column("target_new_a", [2, 1, 0]), Column("target_new_b"), [4, 2, 0]], + "target_old", + ), + ], + ids=["zero_columns", "multiple_columns"], +) +def test_should_throw_illegal_schema_modification( + original_table: TaggedTable, + new_columns: list[Column], + column_name_to_be_replaced: str, +) -> None: + with pytest.raises( + IllegalSchemaModificationError, + match='Target column "target_old" can only be replaced by exactly one new column.', + ): + original_table.replace_column(column_name_to_be_replaced, new_columns) diff --git a/tests/safeds/data/tabular/containers/_table/_tagged_table/test_shuffle_rows.py b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_shuffle_rows.py new file mode 100644 index 000000000..767a6c14f --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_shuffle_rows.py @@ -0,0 +1,37 @@ +import pytest +from safeds.data.tabular.containers import Row, Table, TaggedTable + +from tests.helpers import assert_that_tagged_tables_are_equal + + +@pytest.mark.parametrize( + ("rows", "target_name", "feature_names"), + [ + ( + [ + Row({"feature_a": 0, "feature_b": 3, "no_feature": 6, "target": 9}), + Row({"feature_a": 1, "feature_b": 4, "no_feature": 7, "target": 10}), + Row({"feature_a": 2, "feature_b": 5, "no_feature": 8, "target": 11}), + ], + "target", + ["feature_a", "feature_b"], + ), + ], + ids=["table"], +) +def test_should_shuffle_rows(rows: list[Row], target_name: str, feature_names: list[str]) -> None: + table = TaggedTable._from_table(Table.from_rows(rows), target_name=target_name, feature_names=feature_names) + shuffled = table.shuffle_rows() + assert table.schema == shuffled.schema + assert table.features.column_names == shuffled.features.column_names + assert table.target.name == shuffled.target.name + + # Check that shuffled contains the original rows: + for i in range(table.number_of_rows): + assert shuffled.get_row(i) in rows + + # Assert that table and shuffled are equal again after sorting: + def comparator(r1: Row, r2: Row) -> int: + return 1 if r1.__repr__() < r2.__repr__() else -1 + + assert_that_tagged_tables_are_equal(table.sort_rows(comparator), shuffled.sort_rows(comparator)) diff --git a/tests/safeds/data/tabular/containers/_table/_tagged_table/test_slice_rows.py b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_slice_rows.py new file mode 100644 index 000000000..c4dcfeee2 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_slice_rows.py @@ -0,0 +1,55 @@ +import pytest +from _pytest.python_api import raises +from safeds.data.tabular.containers import TaggedTable +from safeds.exceptions import IndexOutOfBoundsError + +from tests.helpers import assert_that_tagged_tables_are_equal + + +@pytest.mark.parametrize( + ("table", "test_table", "second_test_table"), + [ + ( + TaggedTable( + {"feature": [1, 2, 1], "non_feature": [0, 2, 4], "target": [1, 2, 4]}, + target_name="target", + feature_names=["non_feature"], + ), + TaggedTable( + {"feature": [1, 2], "non_feature": [0, 2], "target": [1, 2]}, + target_name="target", + feature_names=["non_feature"], + ), + TaggedTable( + {"feature": [1, 1], "non_feature": [0, 4], "target": [1, 4]}, + target_name="target", + feature_names=["non_feature"], + ), + ), + ], + ids=["Table with three rows"], +) +def test_should_slice_rows(table: TaggedTable, test_table: TaggedTable, second_test_table: TaggedTable) -> None: + new_table = table.slice_rows(0, 2, 1) + second_new_table = table.slice_rows(0, 3, 2) + third_new_table = table.slice_rows() + assert_that_tagged_tables_are_equal(new_table, test_table) + assert_that_tagged_tables_are_equal(second_new_table, second_test_table) + assert_that_tagged_tables_are_equal(third_new_table, table) + + +@pytest.mark.parametrize( + ("start", "end", "step", "error_message"), + [ + (3, 2, 1, r"There is no element in the range \[3, 2\]"), + (4, 0, 1, r"There is no element in the range \[4, 0\]"), + (0, 4, 1, r"There is no element at index '4'"), + (-4, 0, 1, r"There is no element at index '-4'"), + (0, -4, 1, r"There is no element in the range \[0, -4\]"), + ], +) +def test_should_raise_if_index_out_of_bounds(start: int, end: int, step: int, error_message: str) -> None: + table = TaggedTable({"feature": [1, 2, 1], "target": [1, 2, 4]}, "target") + + with raises(IndexOutOfBoundsError, match=error_message): + table.slice_rows(start, end, step) diff --git a/tests/safeds/data/tabular/containers/_table/_tagged_table/test_sort_columns.py b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_sort_columns.py new file mode 100644 index 000000000..4ecdb78a7 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_sort_columns.py @@ -0,0 +1,55 @@ +from collections.abc import Callable + +import pytest +from safeds.data.tabular.containers import Column, TaggedTable + + +@pytest.mark.parametrize( + ("query", "col1", "col2", "col3", "col4"), + [ + (None, 0, 1, 2, 3), + ( + lambda col1, col2: (col1.name < col2.name) - (col1.name > col2.name), + 3, + 2, + 1, + 0, + ), + ], + ids=["no query", "with query"], +) +def test_should_return_sorted_table( + query: Callable[[Column, Column], int], + col1: int, + col2: int, + col3: int, + col4: int, +) -> None: + columns = [ + Column("col1", ["A", "B", "C", "A", "D"]), + Column("col2", ["Test1", "Test1", "Test3", "Test1", "Test4"]), + Column("col3", [1, 2, 3, 4, 5]), + Column("col4", [2, 3, 1, 4, 6]), + ] + table1 = TaggedTable( + { + "col2": ["Test1", "Test1", "Test3", "Test1", "Test4"], + "col3": [1, 2, 3, 4, 5], + "col4": [2, 3, 1, 4, 6], + "col1": ["A", "B", "C", "A", "D"], + }, + target_name="col1", + feature_names=["col4", "col3"], + ) + if query is not None: + table_sorted = table1.sort_columns(query) + else: + table_sorted = table1.sort_columns() + table_sorted_columns = table_sorted.to_columns() + assert table_sorted.schema == table1.schema + assert table_sorted_columns[0] == columns[col1] + assert table_sorted_columns[1] == columns[col2] + assert table_sorted_columns[2] == columns[col3] + assert table_sorted_columns[3] == columns[col4] + assert table_sorted.features == table1.features + assert table_sorted.target == table1.target diff --git a/tests/safeds/data/tabular/containers/_table/_tagged_table/test_sort_rows.py b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_sort_rows.py new file mode 100644 index 000000000..1cd86c6b4 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_sort_rows.py @@ -0,0 +1,56 @@ +from collections.abc import Callable + +import pytest +from safeds.data.tabular.containers import Row, TaggedTable + +from tests.helpers import assert_that_tagged_tables_are_equal + + +@pytest.mark.parametrize( + ("table", "comparator", "expected"), + [ + ( + TaggedTable({"feature": [3, 2, 1], "non_feature": [1, 1, 1], "target": [0, 0, 0]}, "target"), + lambda row1, row2: row1["feature"] - row2["feature"], + TaggedTable({"feature": [1, 2, 3], "non_feature": [1, 1, 1], "target": [0, 0, 0]}, "target"), + ), + ( + TaggedTable({"feature": [1, 2, 3], "non_feature": [1, 1, 1], "target": [0, 0, 0]}, "target"), + lambda row1, row2: row1["feature"] - row2["feature"], + TaggedTable({"feature": [1, 2, 3], "non_feature": [1, 1, 1], "target": [0, 0, 0]}, "target"), + ), + ], + ids=["unsorted", "already_sorted"], +) +def test_should_sort_table( + table: TaggedTable, + comparator: Callable[[Row, Row], int], + expected: TaggedTable, +) -> None: + table_sorted = table.sort_rows(comparator) + assert_that_tagged_tables_are_equal(table_sorted, expected) + + +@pytest.mark.parametrize( + ("table", "comparator", "table_copy"), + [ + ( + TaggedTable({"feature": [3, 2, 1], "non_feature": [1, 1, 1], "target": [0, 0, 0]}, "target"), + lambda row1, row2: row1["feature"] - row2["feature"], + TaggedTable({"feature": [3, 2, 1], "non_feature": [1, 1, 1], "target": [0, 0, 0]}, "target"), + ), + ( + TaggedTable({"feature": [1, 2, 3], "non_feature": [1, 1, 1], "target": [0, 0, 0]}, "target"), + lambda row1, row2: row1["feature"] - row2["feature"], + TaggedTable({"feature": [1, 2, 3], "non_feature": [1, 1, 1], "target": [0, 0, 0]}, "target"), + ), + ], + ids=["unsorted", "already_sorted"], +) +def test_should_not_modify_original_table( + table: TaggedTable, + comparator: Callable[[Row, Row], int], + table_copy: TaggedTable, +) -> None: + table.sort_rows(comparator) + assert_that_tagged_tables_are_equal(table, table_copy) diff --git a/tests/safeds/data/tabular/containers/_table/_tagged_table/test_target.py b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_target.py new file mode 100644 index 000000000..755721123 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_target.py @@ -0,0 +1,24 @@ +import pytest +from safeds.data.tabular.containers import Column, TaggedTable + + +@pytest.mark.parametrize( + ("tagged_table", "target_column"), + [ + ( + TaggedTable( + { + "A": [1, 4], + "B": [2, 5], + "C": [3, 6], + "T": [0, 1], + }, + target_name="T", + ), + Column("T", [0, 1]), + ), + ], + ids=["target"], +) +def test_should_return_target(tagged_table: TaggedTable, target_column: Column) -> None: + assert tagged_table.target == target_column diff --git a/tests/safeds/data/tabular/containers/_table/_tagged_table/test_transform_column.py b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_transform_column.py new file mode 100644 index 000000000..efcc6bc1c --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/_tagged_table/test_transform_column.py @@ -0,0 +1,73 @@ +import pytest +from safeds.data.tabular.containers import TaggedTable +from safeds.exceptions import UnknownColumnNameError + +from tests.helpers import assert_that_tagged_tables_are_equal + + +@pytest.mark.parametrize( + ("table", "column_name", "table_transformed"), + [ + ( + TaggedTable({"feature_a": [1, 2, 3], "feature_b": [4, 5, 6], "target": [1, 2, 3]}, "target"), + "feature_a", + TaggedTable({"feature_a": [2, 4, 6], "feature_b": [4, 5, 6], "target": [1, 2, 3]}, "target"), + ), + ( + TaggedTable({"feature_a": [1, 2, 3], "feature_b": [4, 5, 6], "target": [1, 2, 3]}, "target"), + "target", + TaggedTable({"feature_a": [1, 2, 3], "feature_b": [4, 5, 6], "target": [2, 4, 6]}, "target"), + ), + ( + TaggedTable( + {"feature_a": [1, 2, 3], "b": [4, 5, 6], "target": [1, 2, 3]}, + target_name="target", + feature_names=["feature_a"], + ), + "b", + TaggedTable( + {"feature_a": [1, 2, 3], "b": [8, 10, 12], "target": [1, 2, 3]}, + target_name="target", + feature_names=["feature_a"], + ), + ), + ], + ids=["transform_feature_column", "transform_target_column", "transform_column_that_is_neither"], +) +def test_should_transform_column(table: TaggedTable, column_name: str, table_transformed: TaggedTable) -> None: + result = table.transform_column(column_name, lambda row: row.get_value(column_name) * 2) + assert_that_tagged_tables_are_equal(result, table_transformed) + + +@pytest.mark.parametrize( + ("table", "column_name"), + [ + ( + TaggedTable( + { + "A": [1, 2, 3], + "B": [4, 5, 6], + "C": ["a", "b", "c"], + }, + "C", + ), + "D", + ), + ( + TaggedTable( + { + "A": [1, 2, 3], + "B": [4, 5, 6], + "C": ["a", "b", "c"], + }, + target_name="C", + feature_names=["A"], + ), + "D", + ), + ], + ids=["has_only_features_and_target", "has_columns_that_are_neither"], +) +def test_should_raise_if_column_not_found(table: TaggedTable, column_name: str) -> None: + with pytest.raises(UnknownColumnNameError, match=rf"Could not find column\(s\) '{column_name}'"): + table.transform_column(column_name, lambda row: row.get_value("A") * 2) diff --git a/tests/safeds/data/tabular/containers/_table/test_add_column.py b/tests/safeds/data/tabular/containers/_table/test_add_column.py index c8fbccabb..ac6430c57 100644 --- a/tests/safeds/data/tabular/containers/_table/test_add_column.py +++ b/tests/safeds/data/tabular/containers/_table/test_add_column.py @@ -1,33 +1,47 @@ import pytest from safeds.data.tabular.containers import Column, Table -from safeds.data.tabular.exceptions import ColumnSizeError, DuplicateColumnNameError -from safeds.data.tabular.typing import ColumnType, Integer, String +from safeds.exceptions import ColumnSizeError, DuplicateColumnNameError @pytest.mark.parametrize( - ("column", "col_type"), + ("table1", "column", "expected"), [ - (Column("col3", ["a", "b", "c"]), String()), - (Column("col3", [0, -1, -2]), Integer()), + ( + Table({"col1": [1, 2, 1], "col2": [1, 2, 4]}), + Column("col3", ["a", "b", "c"]), + Table({"col1": [1, 2, 1], "col2": [1, 2, 4], "col3": ["a", "b", "c"]}), + ), + ( + Table({"col1": [1, 2, 1], "col2": [1, 2, 4]}), + Column("col3", [0, -1, -2]), + Table({"col1": [1, 2, 1], "col2": [1, 2, 4], "col3": [0, -1, -2]}), + ), + ( + Table({}), + Column("col3", []), + Table({"col3": []}), + ), + ( + Table({}), + Column("col3", [1]), + Table({"col3": [1]}), + ), ], + ids=["String", "Integer", "empty with empty column", "empty with filled column"], ) -def test_add_column_valid(column: Column, col_type: ColumnType) -> None: - table1 = Table.from_dict({"col1": [1, 2, 1], "col2": [1, 2, 4]}) +def test_should_add_column(table1: Table, column: Column, expected: Table) -> None: table1 = table1.add_column(column) - assert table1.count_columns() == 3 - assert table1.get_column("col3") == column - assert isinstance(table1.schema.get_type_of_column("col1"), Integer) - assert isinstance(table1.schema.get_type_of_column("col2"), Integer) - assert isinstance(table1.schema.get_type_of_column("col3"), type(col_type)) + # assert table1.schema == expected.schema + assert table1 == expected -def test_add_column_invalid_duplicate_column_name_error() -> None: - table1 = Table.from_dict({"col1": [1, 2, 1], "col2": [1, 2, 4]}) - with pytest.raises(DuplicateColumnNameError): - table1 = table1.add_column(Column("col1", ["a", "b", "c"])) +def test_should_raise_error_if_column_name_exists() -> None: + table1 = Table({"col1": [1, 2, 1], "col2": [1, 2, 4]}) + with pytest.raises(DuplicateColumnNameError, match=r"Column 'col1' already exists."): + table1.add_column(Column("col1", ["a", "b", "c"])) -def test_add_column_invalid_column_size_error() -> None: - table1 = Table.from_dict({"col1": [1, 2, 1], "col2": [1, 2, 4]}) - with pytest.raises(ColumnSizeError): - table1 = table1.add_column(Column("col3", ["a", "b", "c", "d"])) +def test_should_raise_error_if_column_size_invalid() -> None: + table1 = Table({"col1": [1, 2, 1], "col2": [1, 2, 4]}) + with pytest.raises(ColumnSizeError, match=r"Expected a column of size 3 but got column of size 4."): + table1.add_column(Column("col3", ["a", "b", "c", "d"])) diff --git a/tests/safeds/data/tabular/containers/_table/test_add_columns.py b/tests/safeds/data/tabular/containers/_table/test_add_columns.py index f701bd7a7..07485a89e 100644 --- a/tests/safeds/data/tabular/containers/_table/test_add_columns.py +++ b/tests/safeds/data/tabular/containers/_table/test_add_columns.py @@ -1,31 +1,94 @@ +import pytest from safeds.data.tabular.containers import Column, Table -from safeds.data.tabular.typing import Integer, String - - -def test_add_columns_valid() -> None: - table1 = Table.from_dict({"col1": [1, 2, 1], "col2": [1, 2, 4]}) - col3 = Column("col3", [0, -1, -2]) - col4 = Column("col4", ["a", "b", "c"]) - table1 = table1.add_columns([col3, col4]) - assert table1.count_columns() == 4 - assert table1.get_column("col3") == col3 - assert table1.get_column("col4") == col4 - assert isinstance(table1.schema.get_type_of_column("col1"), Integer) - assert isinstance(table1.schema.get_type_of_column("col2"), Integer) - assert isinstance(table1.schema.get_type_of_column("col3"), Integer) - assert isinstance(table1.schema.get_type_of_column("col4"), String) - - -def test_add_columns_table_valid() -> None: - table1 = Table.from_dict({"col1": [1, 2, 1], "col2": [1, 2, 4]}) - col3 = Column("col3", [0, -1, -2]) - col4 = Column("col4", ["a", "b", "c"]) - table2 = Table.from_columns([col3, col4]) +from safeds.exceptions import ColumnSizeError, DuplicateColumnNameError + + +@pytest.mark.parametrize( + ("table1", "columns", "expected"), + [ + ( + Table({"col1": [1, 2, 1], "col2": [1, 2, 4]}), + [Column("col3", [0, -1, -2]), Column("col4", ["a", "b", "c"])], + Table({"col1": [1, 2, 1], "col2": [1, 2, 4], "col3": [0, -1, -2], "col4": ["a", "b", "c"]}), + ), + ( + Table({}), + [Column("col3", []), Column("col4", [])], + Table({"col3": [], "col4": []}), + ), + ( + Table({}), + [Column("col3", [1]), Column("col4", [2])], + Table({"col3": [1], "col4": [2]}), + ), + ], + ids=["add 2 columns", "empty with empty column", "empty with filled column"], +) +def test_should_add_columns(table1: Table, columns: list[Column], expected: Table) -> None: + table1 = table1.add_columns(columns) + # assert table1.schema == expected.schema + assert table1 == expected + + +@pytest.mark.parametrize( + ("table1", "table2", "expected"), + [ + ( + Table({"col1": [1, 2, 1], "col2": [1, 2, 4]}), + Table({"col3": [0, -1, -2], "col4": ["a", "b", "c"]}), + Table({"col1": [1, 2, 1], "col2": [1, 2, 4], "col3": [0, -1, -2], "col4": ["a", "b", "c"]}), + ), + (Table(), Table({"col1": [1, 2], "col2": [60, 2]}), Table({"col1": [1, 2], "col2": [60, 2]})), + ( + Table({"col1": [1, 2], "col2": [60, 2]}), + Table(), + Table({"col1": [1, 2], "col2": [60, 2]}), + ), + (Table({"yeet": [], "col": []}), Table({"gg": []}), Table({"yeet": [], "col": [], "gg": []})), + ], + ids=["add a table with 2 columns", "empty add filled", "filled add empty", "rowless"], +) +def test_should_add_columns_from_table(table1: Table, table2: Table, expected: Table) -> None: table1 = table1.add_columns(table2) - assert table1.count_columns() == 4 - assert table1.get_column("col3") == col3 - assert table1.get_column("col4") == col4 - assert isinstance(table1.schema.get_type_of_column("col1"), Integer) - assert isinstance(table1.schema.get_type_of_column("col2"), Integer) - assert isinstance(table1.schema.get_type_of_column("col3"), Integer) - assert isinstance(table1.schema.get_type_of_column("col4"), String) + assert table1.schema == expected.schema + assert table1 == expected + + +@pytest.mark.parametrize( + ("table", "columns", "error_message_regex"), + [ + ( + Table({"col1": [1, 2, 1], "col2": [1, 2, 4]}), + [Column("col3", ["a", "b", "c", "d"]), Column("col4", ["e", "f", "g", "h"])], + r"Expected a column of size 3 but got column of size 4.", + ), + ], + ids=["Two Columns with too many values"], +) +def test_should_raise_error_if_column_size_invalid( + table: Table, + columns: list[Column] | Table, + error_message_regex: str, +) -> None: + with pytest.raises(ColumnSizeError, match=error_message_regex): + table.add_columns(columns) + + +@pytest.mark.parametrize( + ("table", "columns", "error_message_regex"), + [ + ( + Table({"col1": [1, 2, 1], "col2": [1, 2, 4]}), + [Column("col2", ["a", "b", "c"]), Column("col3", [2, 3, 4])], + r"Column 'col2' already exists.", + ), + ], + ids=["Column already exists"], +) +def test_should_raise_error_if_column_name_in_result_column( + table: Table, + columns: list[Column] | Table, + error_message_regex: str, +) -> None: + with pytest.raises(DuplicateColumnNameError, match=error_message_regex): + table.add_columns(columns) diff --git a/tests/safeds/data/tabular/containers/_table/test_add_row.py b/tests/safeds/data/tabular/containers/_table/test_add_row.py index 0832949c7..d99792a43 100644 --- a/tests/safeds/data/tabular/containers/_table/test_add_row.py +++ b/tests/safeds/data/tabular/containers/_table/test_add_row.py @@ -1,23 +1,34 @@ +import pytest from _pytest.python_api import raises from safeds.data.tabular.containers import Row, Table -from safeds.data.tabular.exceptions import SchemaMismatchError -from safeds.data.tabular.typing import Integer, Schema, String +from safeds.exceptions import SchemaMismatchError -def test_add_row_valid() -> None: - table1 = Table.from_dict({"col1": [1, 2, 1], "col2": [1, 2, 4]}) - row = Row([5, 6], table1.schema) - table1 = table1.add_row(row) - assert table1.count_rows() == 4 - assert table1.get_row(3) == row - assert table1.schema == row._schema +@pytest.mark.parametrize( + ("table", "row", "expected"), + [ + ( + Table({"col1": [1, 2, 1], "col2": [1, 2, 4]}), + Row({"col1": 5, "col2": 6}), + Table({"col1": [1, 2, 1, 5], "col2": [1, 2, 4, 6]}), + ), + (Table({"col2": [], "col4": []}), Row({"col2": 5, "col4": 6}), Table({"col2": [5], "col4": [6]})), + (Table(), Row({"col2": 5, "col4": 6}), Table({"col2": [5], "col4": [6]})), + ], + ids=["add row", "add row to rowless table", "add row to empty table"], +) +def test_should_add_row(table: Table, row: Row, expected: Table) -> None: + table = table.add_row(row) + assert table == expected -def test_add_row_invalid() -> None: - table1 = Table.from_dict({"col1": [1, 2, 1], "col2": [1, 2, 4]}) - row = Row( - [5, "Hallo"], - Schema({"col1": Integer(), "col2": String()}), - ) - with raises(SchemaMismatchError): - table1 = table1.add_row(row) +def test_should_raise_error_if_row_schema_invalid() -> None: + table1 = Table({"col1": [1, 2, 1], "col2": [1, 2, 4]}) + row = Row({"col1": 5, "col2": "Hallo"}) + with raises(SchemaMismatchError, match=r"Failed because at least two schemas didn't match."): + table1.add_row(row) + + +def test_should_raise_schema_mismatch() -> None: + with raises(SchemaMismatchError, match=r"Failed because at least two schemas didn't match."): + Table({"a": [], "b": []}).add_row(Row({"beer": None, "rips": None})) diff --git a/tests/safeds/data/tabular/containers/_table/test_add_rows.py b/tests/safeds/data/tabular/containers/_table/test_add_rows.py index d4687f154..2bdf52624 100644 --- a/tests/safeds/data/tabular/containers/_table/test_add_rows.py +++ b/tests/safeds/data/tabular/containers/_table/test_add_rows.py @@ -1,26 +1,72 @@ +import pytest +from _pytest.python_api import raises from safeds.data.tabular.containers import Row, Table +from safeds.exceptions import SchemaMismatchError -def test_add_rows_valid() -> None: - table1 = Table.from_dict({"col1": ["a", "b", "c"], "col2": [1, 2, 4]}) - row1 = Row(["d", 6], table1.schema) - row2 = Row(["e", 8], table1.schema) - table1 = table1.add_rows([row1, row2]) - assert table1.count_rows() == 5 - assert table1.get_row(3) == row1 - assert table1.get_row(4) == row2 - assert table1.schema == row1._schema - assert table1.schema == row2._schema +@pytest.mark.parametrize( + ("table1", "rows", "table2"), + [ + ( + Table({"col1": ["a", "b", "c"], "col2": [1, 2, 4]}), + [Row({"col1": "d", "col2": 6}), Row({"col1": "e", "col2": 8})], + Table({"col1": ["a", "b", "c", "d", "e"], "col2": [1, 2, 4, 6, 8]}), + ), + ( + Table(), + [Row({"col1": "d", "col2": 6}), Row({"col1": "e", "col2": 8})], + Table({"col1": ["d", "e"], "col2": [6, 8]}), + ), + ], + ids=["Rows with string and integer values", "empty"], +) +def test_should_add_rows(table1: Table, rows: list[Row], table2: Table) -> None: + table1 = table1.add_rows(rows) + assert table1.schema == table2.schema + assert table1 == table2 -def test_add_rows_table_valid() -> None: - table1 = Table.from_dict({"col1": [1, 2, 1], "col2": [1, 2, 4]}) - row1 = Row([5, 6], table1.schema) - row2 = Row([7, 8], table1.schema) - table2 = Table.from_rows([row1, row2]) +@pytest.mark.parametrize( + ("table1", "table2", "expected"), + [ + ( + Table({"col1": [1, 2, 1], "col2": [1, 2, 4]}), + Table({"col1": [5, 7], "col2": [6, 8]}), + Table({"col1": [1, 2, 1, 5, 7], "col2": [1, 2, 4, 6, 8]}), + ), + ( + Table({"col1": [2], "yikes": [5]}), + Table(), + Table({"col1": [2], "yikes": [5]}), + ), + ( + Table(), + Table({"col1": [2], "yikes": [5]}), + Table({"col1": [2], "yikes": [5]}), + ), + ( + Table({"col1": [], "yikes": []}), + Table({"col1": [], "yikes": []}), + Table({"col1": [], "yikes": []}), + ), + ], + ids=["Rows from table", "add empty to table", "add on empty table", "rowless"], +) +def test_should_add_rows_from_table(table1: Table, table2: Table, expected: Table) -> None: table1 = table1.add_rows(table2) - assert table1.count_rows() == 5 - assert table1.get_row(3) == row1 - assert table1.get_row(4) == row2 - assert table1.schema == row1._schema - assert table1.schema == row2._schema + assert table1.schema == expected.schema + assert table1 == expected + + +def test_should_raise_error_if_row_schema_invalid() -> None: + table1 = Table({"col1": [1, 2, 1], "col2": [1, 2, 4]}) + row = [Row({"col1": 2, "col2": 4}), Row({"col1": 5, "col2": "Hallo"})] + with pytest.raises(SchemaMismatchError, match=r"Failed because at least two schemas didn't match."): + table1.add_rows(row) + + +def test_should_raise_schema_mismatch() -> None: + with raises(SchemaMismatchError, match=r"Failed because at least two schemas didn't match."): + Table({"a": [], "b": []}).add_rows([Row({"a": None, "b": None}), Row({"beer": None, "rips": None})]) + with raises(SchemaMismatchError, match=r"Failed because at least two schemas didn't match."): + Table({"a": [], "b": []}).add_rows([Row({"beer": None, "rips": None}), Row({"a": None, "b": None})]) diff --git a/tests/safeds/data/tabular/containers/_table/test_as_table.py b/tests/safeds/data/tabular/containers/_table/test_as_table.py new file mode 100644 index 000000000..7f72e76e8 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/test_as_table.py @@ -0,0 +1,9 @@ +import pytest +from safeds.data.tabular.containers import Table + + +@pytest.mark.parametrize("table", [Table({"col1": [1, 2], "col2:": [3, 4]}), Table()], ids=["table", "empty"]) +def should_return_table(table: Table) -> None: + new_table = table._as_table() + assert table.schema == new_table.schema + assert table == new_table diff --git a/tests/safeds/data/tabular/containers/_table/test_column_names.py b/tests/safeds/data/tabular/containers/_table/test_column_names.py new file mode 100644 index 000000000..2fde17dff --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/test_column_names.py @@ -0,0 +1,15 @@ +import pytest +from safeds.data.tabular.containers import Table + + +@pytest.mark.parametrize( + ("table", "expected"), + [ + (Table({"col1": [1], "col2": [1]}), ["col1", "col2"]), + (Table({"col": [], "gg": []}), ["col", "gg"]), + (Table(), []), + ], + ids=["Integer", "rowless", "empty"], +) +def test_should_compare_column_names(table: Table, expected: list) -> None: + assert table.column_names == expected diff --git a/tests/safeds/data/tabular/containers/_table/test_copy.py b/tests/safeds/data/tabular/containers/_table/test_copy.py new file mode 100644 index 000000000..25c7e762f --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/test_copy.py @@ -0,0 +1,13 @@ +import pytest +from safeds.data.tabular.containers import Table + + +@pytest.mark.parametrize( + "table", + [Table(), Table({"a": [], "b": []}), Table({"a": ["a", 3, 0.1], "b": [True, False, None]})], + ids=["empty", "empty-rows", "normal"], +) +def test_should_copy_table(table: Table) -> None: + copied = table._copy() + assert copied == table + assert copied is not table diff --git a/tests/safeds/data/tabular/containers/_table/test_correlation_heatmap.py b/tests/safeds/data/tabular/containers/_table/test_correlation_heatmap.py deleted file mode 100644 index 7e579b189..000000000 --- a/tests/safeds/data/tabular/containers/_table/test_correlation_heatmap.py +++ /dev/null @@ -1,15 +0,0 @@ -import _pytest -import matplotlib.pyplot as plt -from safeds.data.tabular.containers import Table - - -def test_correlation_heatmap_non_numeric(monkeypatch: _pytest.monkeypatch) -> None: - monkeypatch.setattr(plt, "show", lambda: None) - table = Table.from_dict({"A": [1, 2, "A"], "B": [1, 2, 3]}) - table.correlation_heatmap() - - -def test_correlation_heatmap(monkeypatch: _pytest.monkeypatch) -> None: - monkeypatch.setattr(plt, "show", lambda: None) - table = Table.from_dict({"A": [1, 2, 3.5], "B": [2, 4, 7]}) - table.correlation_heatmap() diff --git a/tests/safeds/data/tabular/containers/_table/test_count_columns.py b/tests/safeds/data/tabular/containers/_table/test_count_columns.py deleted file mode 100644 index 408380785..000000000 --- a/tests/safeds/data/tabular/containers/_table/test_count_columns.py +++ /dev/null @@ -1,14 +0,0 @@ -import pytest -from safeds.data.tabular.containers import Table - - -@pytest.mark.parametrize( - ("table", "expected"), - [ - (Table.from_dict({}), 0), - (Table.from_dict({"col1": []}), 1), - (Table.from_dict({"col1": [], "col2": []}), 2), - ], -) -def test_count_columns(table: Table, expected: int) -> None: - assert table.count_columns() == expected diff --git a/tests/safeds/data/tabular/containers/_table/test_count_rows.py b/tests/safeds/data/tabular/containers/_table/test_count_rows.py deleted file mode 100644 index 3880b6fe9..000000000 --- a/tests/safeds/data/tabular/containers/_table/test_count_rows.py +++ /dev/null @@ -1,14 +0,0 @@ -import pytest -from safeds.data.tabular.containers import Table - - -@pytest.mark.parametrize( - ("table", "expected"), - [ - (Table.from_dict({}), 0), - (Table.from_dict({"col1": [1]}), 1), - (Table.from_dict({"col1": [1, 2], "col2": [3, 4]}), 2), - ], -) -def test_count_rows(table: Table, expected: int) -> None: - assert table.count_rows() == expected diff --git a/tests/safeds/data/tabular/containers/_table/test_dataframe.py b/tests/safeds/data/tabular/containers/_table/test_dataframe.py new file mode 100644 index 000000000..5a49dcefd --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/test_dataframe.py @@ -0,0 +1,31 @@ +import pytest +from pandas.core.interchange.from_dataframe import from_dataframe +from safeds.data.tabular.containers import Table + + +@pytest.mark.parametrize( + "table", + [ + Table(), + Table({"a": [1, 2], "b": [3, 4]}), + ], + ids=[ + "empty", + "non-empty", + ], +) +def test_should_restore_table_from_exchange_object(table: Table) -> None: + exchange_object = table.__dataframe__() + restored = Table._from_pandas_dataframe(from_dataframe(exchange_object)) + + assert restored.schema == table.schema + assert restored == table + + +def test_should_raise_error_if_allow_copy_is_false() -> None: + table = Table() + with pytest.raises( + NotImplementedError, + match=r"For the moment we need to copy the data, so `allow_copy` must be True.", + ): + table.__dataframe__(allow_copy=False) diff --git a/tests/safeds/data/tabular/containers/_table/test_eq.py b/tests/safeds/data/tabular/containers/_table/test_eq.py new file mode 100644 index 000000000..21f185340 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/test_eq.py @@ -0,0 +1,54 @@ +from typing import Any + +import pytest +from safeds.data.tabular.containers import Row, Table + + +@pytest.mark.parametrize( + ("table1", "table2", "expected"), + [ + (Table(), Table(), True), + (Table({"a": [], "b": []}), Table({"a": [], "b": []}), True), + (Table({"col1": [1]}), Table({"col1": [1]}), True), + (Table({"col1": [1]}), Table({"col2": [1]}), False), + (Table({"col1": [1, 2, 3]}), Table({"col1": [1, 1, 3]}), False), + (Table({"col1": [1, 2, 3]}), Table({"col1": ["1", "2", "3"]}), False), + ], + ids=[ + "empty table", + "rowless table", + "equal tables", + "different column names", + "different values", + "different types", + ], +) +def test_should_return_whether_two_tables_are_equal(table1: Table, table2: Table, expected: bool) -> None: + assert (table1.__eq__(table2)) == expected + + +@pytest.mark.parametrize( + "table", + [Table(), Table({"col1": [1]})], + ids=[ + "empty", + "non-empty", + ], +) +def test_should_return_true_if_objects_are_identical(table: Table) -> None: + assert (table.__eq__(table)) is True + + +@pytest.mark.parametrize( + ("table", "other"), + [ + (Table({"col1": [1]}), None), + (Table({"col1": [1]}), Row()), + ], + ids=[ + "Table vs. None", + "Table vs. Row", + ], +) +def test_should_return_not_implemented_if_other_is_not_table(table: Table, other: Any) -> None: + assert (table.__eq__(other)) is NotImplemented diff --git a/tests/safeds/data/tabular/containers/_table/test_filter_rows.py b/tests/safeds/data/tabular/containers/_table/test_filter_rows.py index a163c4c53..ad8c4f45e 100644 --- a/tests/safeds/data/tabular/containers/_table/test_filter_rows.py +++ b/tests/safeds/data/tabular/containers/_table/test_filter_rows.py @@ -1,18 +1,41 @@ +import pandas as pd import pytest from safeds.data.tabular.containers import Table +from safeds.data.tabular.typing import ColumnType, Integer, Schema -def test_filter_rows_valid() -> None: - table = Table.from_dict({"col1": [1, 2, 1], "col2": [1, 2, 4]}) - result_table = table.filter_rows(lambda row: row.get_value("col1") == 1) - assert result_table.get_column("col1").get_value(0) == 1 - assert result_table.get_column("col2").get_value(1) == 4 - assert result_table.count_columns() == 2 - assert result_table.count_rows() == 2 +@pytest.mark.parametrize( + ("table1", "filter_column", "filter_value", "table2"), + [ + ( + Table({"col1": [1, 2, 1], "col2": [1, 2, 4]}), + "col1", + 1, + Table({"col1": [1, 1], "col2": [1, 4]}), + ), + ( + Table({"col1": [3, 2, 4], "col2": [1, 2, 4]}), + "col1", + 1, + Table._from_pandas_dataframe(pd.DataFrame(), Schema({"col1": Integer(), "col2": Integer()})), + ), + ( + Table(), + "col1", + 1, + Table._from_pandas_dataframe(pd.DataFrame(), Schema({})), + ), + ], + ids=["filter for col1 = 1", "no finding", "empty table"], +) +def test_should_filter_rows(table1: Table, filter_column: str, filter_value: ColumnType, table2: Table) -> None: + table1 = table1.filter_rows(lambda row: row.get_value(filter_column) == filter_value) + assert table1.schema == table2.schema + assert table2 == table1 # noinspection PyTypeChecker -def test_filter_rows_invalid() -> None: - table = Table.from_dict({"col1": [1, 2, 3], "col2": [1, 1, 4]}) - with pytest.raises(TypeError): +def test_should_raise_error_if_column_type_invalid() -> None: + table = Table({"col1": [1, 2, 3], "col2": [1, 1, 4]}) + with pytest.raises(TypeError, match=r"'Series' object is not callable"): table.filter_rows(table.get_column("col1")._data > table.get_column("col2")._data) diff --git a/tests/safeds/data/tabular/containers/_table/test_from_columns.py b/tests/safeds/data/tabular/containers/_table/test_from_columns.py index 338c71403..b0a6feec9 100644 --- a/tests/safeds/data/tabular/containers/_table/test_from_columns.py +++ b/tests/safeds/data/tabular/containers/_table/test_from_columns.py @@ -1,6 +1,6 @@ import pytest from safeds.data.tabular.containers import Column, Table -from safeds.data.tabular.typing import Integer, Schema +from safeds.exceptions import ColumnLengthMismatchError, DuplicateColumnNameError @pytest.mark.parametrize( @@ -11,9 +11,30 @@ Column("A", [1, 4]), Column("B", [2, 5]), ], - Table([[1, 2], [4, 5]], Schema({"A": Integer(), "B": Integer()})), + Table( + { + "A": [1, 4], + "B": [2, 5], + }, + ), ), + ([], Table()), ], + ids=["2 Columns", "empty"], ) -def test_from_columns(columns: list[Column], expected: Table) -> None: +def test_should_create_table_from_list_of_columns(columns: list[Column], expected: Table) -> None: + assert Table.from_columns(columns).schema == expected.schema assert Table.from_columns(columns) == expected + + +def test_should_raise_error_if_column_length_mismatch() -> None: + with pytest.raises( + ColumnLengthMismatchError, + match=r"The length of at least one column differs: \ncol1: 3\ncol2: 4", + ): + Table.from_columns([Column("col1", [5, 2, 3]), Column("col2", [5, 3, 4, 1])]) + + +def test_should_raise_error_if_duplicate_column_name() -> None: + with pytest.raises(DuplicateColumnNameError, match=r"Column 'col1' already exists."): + Table.from_columns([Column("col1", [5, 2, 3]), Column("col1", [5, 3, 4])]) diff --git a/tests/safeds/data/tabular/containers/_table/test_from_csv_file.py b/tests/safeds/data/tabular/containers/_table/test_from_csv_file.py index 58a19f0c2..bc7105ae7 100644 --- a/tests/safeds/data/tabular/containers/_table/test_from_csv_file.py +++ b/tests/safeds/data/tabular/containers/_table/test_from_csv_file.py @@ -1,15 +1,60 @@ +from pathlib import Path + import pytest from safeds.data.tabular.containers import Table +from safeds.exceptions import WrongFileExtensionError from tests.helpers import resolve_resource_path -def test_from_csv_file_valid() -> None: - table = Table.from_csv_file(resolve_resource_path("test_table_from_csv_file.csv")) - assert table.get_column("A").get_value(0) == 1 - assert table.get_column("B").get_value(0) == 2 +@pytest.mark.parametrize( + ("path", "expected"), + [ + ("table.csv", Table({"A": [1], "B": [2]})), + (Path("table.csv"), Table({"A": [1], "B": [2]})), + ("emptytable.csv", Table()), + ], + ids=["by String", "by path", "empty"], +) +def test_should_create_table_from_csv_file(path: str | Path, expected: Table) -> None: + table = Table.from_csv_file(resolve_resource_path(path)) + assert table.schema == expected.schema + assert table == expected + + +@pytest.mark.parametrize( + ("path", "expected_error_message"), + [ + ("test_table_from_csv_file_invalid.csv", r"test_table_from_csv_file_invalid.csv\" does not exist"), + (Path("test_table_from_csv_file_invalid.csv"), r"test_table_from_csv_file_invalid.csv\" does not exist"), + ], + ids=["by String", "by path"], +) +def test_should_raise_error_if_file_not_found(path: str | Path, expected_error_message: str) -> None: + with pytest.raises(FileNotFoundError, match=expected_error_message): + Table.from_csv_file(resolve_resource_path(path)) -def test_from_csv_file_invalid() -> None: - with pytest.raises(FileNotFoundError): - Table.from_csv_file(resolve_resource_path("test_table_from_csv_file_invalid.csv")) +@pytest.mark.parametrize( + ("path", "expected_error_message"), + [ + ( + "invalid_file_extension.file_extension", + ( + r"invalid_file_extension.file_extension has a wrong file extension. Please provide a file with the" + r" following extension\(s\): .csv" + ), + ), + ( + Path("invalid_file_extension.file_extension"), + ( + r"invalid_file_extension.file_extension has a wrong file extension. Please provide a file with the" + r" following extension\(s\): .csv" + ), + ), + ], + ids=["by String", "by path"], +) +def test_should_raise_error_if_wrong_file_extension(path: str | Path, expected_error_message: str) -> None: + with pytest.raises(WrongFileExtensionError, match=expected_error_message): + Table.from_csv_file(resolve_resource_path(path)) diff --git a/tests/safeds/data/tabular/containers/_table/test_from_dict.py b/tests/safeds/data/tabular/containers/_table/test_from_dict.py new file mode 100644 index 000000000..78ec02d4a --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/test_from_dict.py @@ -0,0 +1,37 @@ +from typing import Any + +import pytest +from safeds.data.tabular.containers import Table +from safeds.exceptions import ColumnLengthMismatchError + + +@pytest.mark.parametrize( + ("data", "expected"), + [ + ( + {}, + Table(), + ), + ( + { + "a": [1], + "b": [2], + }, + Table.from_dict( + { + "a": [1], + "b": [2], + }, + ), + ), + ], + ids=["empty", "with values"], +) +def test_should_create_table_from_dict(data: dict[str, list[Any]], expected: Table) -> None: + assert Table.from_dict(data).schema == expected.schema + assert Table.from_dict(data) == expected + + +def test_should_raise_error_if_columns_have_different_lengths() -> None: + with pytest.raises(ColumnLengthMismatchError, match=r"The length of at least one column differs: \na: 2\nb: 1"): + Table.from_dict({"a": [1, 2], "b": [3]}) diff --git a/tests/safeds/data/tabular/containers/_table/test_from_excel_file.py b/tests/safeds/data/tabular/containers/_table/test_from_excel_file.py new file mode 100644 index 000000000..b6b9eb07d --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/test_from_excel_file.py @@ -0,0 +1,68 @@ +from pathlib import Path + +import pytest +from safeds.data.tabular.containers import Table +from safeds.exceptions import WrongFileExtensionError + +from tests.helpers import resolve_resource_path + + +@pytest.mark.parametrize( + ("path", "expected"), + [ + ( + resolve_resource_path("./dummy_excel_file.xlsx"), + Table( + { + "A": [1], + "B": [2], + }, + ), + ), + ( + Path(resolve_resource_path("./dummy_excel_file.xlsx")), + Table( + { + "A": [1], + "B": [2], + }, + ), + ), + (resolve_resource_path("./empty_excel_file.xlsx"), Table()), + ], + ids=["string path", "object path", "empty file"], +) +def test_should_create_table_from_excel_file(path: str | Path, expected: Table) -> None: + table = Table.from_excel_file(path) + assert table.schema == expected.schema + assert table == expected + + +def test_should_raise_if_file_not_found() -> None: + with pytest.raises(FileNotFoundError, match=r"test_table_from_excel_file_invalid.xls\" does not exist"): + Table.from_excel_file(resolve_resource_path("test_table_from_excel_file_invalid.xls")) + + +@pytest.mark.parametrize( + ("path", "expected_error_message"), + [ + ( + "invalid_file_extension.file_extension", + ( + r"invalid_file_extension.file_extension has a wrong file extension. Please provide a file with the" + r" following extension\(s\): \['.xls', '.xlsx', '.xlsm', '.xlsb', '.odf', '.ods', '.odt'\]" + ), + ), + ( + Path("invalid_file_extension.file_extension"), + ( + r"invalid_file_extension.file_extension has a wrong file extension. Please provide a file with the" + r" following extension\(s\): \['.xls', '.xlsx', '.xlsm', '.xlsb', '.odf', '.ods', '.odt'\]" + ), + ), + ], + ids=["by String", "by path"], +) +def test_should_raise_error_if_wrong_file_extension(path: str | Path, expected_error_message: str) -> None: + with pytest.raises(WrongFileExtensionError, match=expected_error_message): + Table.from_excel_file(resolve_resource_path(path)) diff --git a/tests/safeds/data/tabular/containers/_table/test_from_json_file.py b/tests/safeds/data/tabular/containers/_table/test_from_json_file.py index f12ca2a1f..b94408a54 100644 --- a/tests/safeds/data/tabular/containers/_table/test_from_json_file.py +++ b/tests/safeds/data/tabular/containers/_table/test_from_json_file.py @@ -1,15 +1,60 @@ +from pathlib import Path + import pytest from safeds.data.tabular.containers import Table +from safeds.exceptions import WrongFileExtensionError from tests.helpers import resolve_resource_path -def test_from_json_file_valid() -> None: - table = Table.from_json_file(resolve_resource_path("test_table_from_json_file.json")) - assert table.get_column("A").get_value(0) == 1 - assert table.get_column("B").get_value(0) == 2 +@pytest.mark.parametrize( + ("path", "expected"), + [ + ("table.json", Table({"A": [1], "B": [2]})), + (Path("table.json"), Table({"A": [1], "B": [2]})), + (Path("emptytable.json"), Table()), + ], + ids=["by string", "by path", "empty"], +) +def test_should_create_table_from_json_file(path: str | Path, expected: Table) -> None: + table = Table.from_json_file(resolve_resource_path(path)) + assert table.schema == expected.schema + assert table == expected + + +@pytest.mark.parametrize( + ("path", "expected_error_message"), + [ + ("test_table_from_json_file_invalid.json", r"test_table_from_json_file_invalid.json\" does not exist"), + (Path("test_table_from_json_file_invalid.json"), r"test_table_from_json_file_invalid.json\" does not exist"), + ], + ids=["by string", "by path"], +) +def test_should_raise_error_if_file_not_found(path: str | Path, expected_error_message: str) -> None: + with pytest.raises(FileNotFoundError, match=expected_error_message): + Table.from_json_file(resolve_resource_path(path)) -def test_from_json_file_invalid() -> None: - with pytest.raises(FileNotFoundError): - Table.from_json_file(resolve_resource_path("test_table_from_json_file_invalid.json")) +@pytest.mark.parametrize( + ("path", "expected_error_message"), + [ + ( + "invalid_file_extension.file_extension", + ( + r"invalid_file_extension.file_extension has a wrong file extension. Please provide a file with the" + r" following extension\(s\): .json" + ), + ), + ( + Path("invalid_file_extension.file_extension"), + ( + r"invalid_file_extension.file_extension has a wrong file extension. Please provide a file with the" + r" following extension\(s\): .json" + ), + ), + ], + ids=["by String", "by path"], +) +def test_should_raise_error_if_wrong_file_extension(path: str | Path, expected_error_message: str) -> None: + with pytest.raises(WrongFileExtensionError, match=expected_error_message): + Table.from_json_file(resolve_resource_path(path)) diff --git a/tests/safeds/data/tabular/containers/_table/test_from_pandas_dataframe.py b/tests/safeds/data/tabular/containers/_table/test_from_pandas_dataframe.py new file mode 100644 index 000000000..6ed19a906 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/test_from_pandas_dataframe.py @@ -0,0 +1,78 @@ +import pandas as pd +import pytest +from safeds.data.tabular.containers import Table +from safeds.data.tabular.typing import Integer, RealNumber, Schema, String + + +@pytest.mark.parametrize( + ("dataframe", "schema", "expected", "expected_table"), + [ + (pd.DataFrame({"col1": [0]}), Schema({"col1": Integer()}), Schema({"col1": Integer()}), Table({"col1": [0]})), + ( + pd.DataFrame({"col1": [0], "col2": ["a"]}), + Schema({"col1": Integer(), "col2": String()}), + Schema({"col1": Integer(), "col2": String()}), + Table({"col1": [0], "col2": ["a"]}), + ), + ( + pd.DataFrame({"col1": [0, 1.1]}), + Schema({"col1": String()}), + Schema({"col1": String()}), + Table({"col1": [0, 1.1]}), + ), + ( + pd.DataFrame({"col1": [0, 1.1], "col2": ["a", "b"]}), + Schema({"col1": String(), "col2": String()}), + Schema({"col1": String(), "col2": String()}), + Table({"col1": [0, 1.1], "col2": ["a", "b"]}), + ), + (pd.DataFrame(), Schema({}), Schema({}), Table()), + ], + ids=["one row, one column", "one row, two columns", "two rows, one column", "two rows, two columns", "empty"], +) +def test_should_use_the_schema_if_passed( + dataframe: pd.DataFrame, + schema: Schema, + expected: Schema, + expected_table: Table, +) -> None: + table = Table._from_pandas_dataframe(dataframe, schema) + assert table._schema == expected + assert table == expected_table + + +@pytest.mark.parametrize( + ("dataframe", "expected"), + [ + ( + pd.DataFrame({"col1": [0]}), + Schema({"col1": Integer()}), + ), + ( + pd.DataFrame({"col1": [0], "col2": ["a"]}), + Schema({"col1": Integer(), "col2": String()}), + ), + ( + pd.DataFrame({"col1": [0, 1.1]}), + Schema({"col1": RealNumber()}), + ), + ( + pd.DataFrame({"col1": [0, 1.1], "col2": ["a", "b"]}), + Schema({"col1": RealNumber(), "col2": String()}), + ), + ], + ids=[ + "one row, one column", + "one row, two columns", + "two rows, one column", + "two rows, two columns", + ], +) +def test_should_infer_the_schema_if_not_passed(dataframe: pd.DataFrame, expected: Schema) -> None: + table = Table._from_pandas_dataframe(dataframe) + assert table._schema == expected + + +def test_should_be_able_to_handle_empty_dataframe_with_given_schema() -> None: + table = Table._from_pandas_dataframe(pd.DataFrame(), Schema({"col1": Integer(), "col2": Integer()})) + table.get_column("col1") diff --git a/tests/safeds/data/tabular/containers/_table/test_from_rows.py b/tests/safeds/data/tabular/containers/_table/test_from_rows.py index 65eacc902..43954d072 100644 --- a/tests/safeds/data/tabular/containers/_table/test_from_rows.py +++ b/tests/safeds/data/tabular/containers/_table/test_from_rows.py @@ -1,18 +1,38 @@ import pytest from safeds.data.tabular.containers import Row, Table -from safeds.data.tabular.exceptions import MissingDataError +from safeds.exceptions import SchemaMismatchError -from tests.helpers import resolve_resource_path +@pytest.mark.parametrize( + ("rows", "expected"), + [ + ( + [], + Table({}), + ), + ( + [ + Row({"A": 1, "B": 4, "C": "d"}), + Row({"A": 2, "B": 5, "C": "e"}), + Row({"A": 3, "B": 6, "C": "f"}), + ], + Table( + { + "A": [1, 2, 3], + "B": [4, 5, 6], + "C": ["d", "e", "f"], + }, + ), + ), + ], + ids=["empty", "non-empty"], +) +def test_should_create_table_from_rows(rows: list[Row], expected: Table) -> None: + assert Table.from_rows(rows).schema == expected.schema + assert Table.from_rows(rows) == expected -def test_from_rows() -> None: - table_expected = Table.from_csv_file(resolve_resource_path("test_row_table.csv")) - rows_is: list[Row] = table_expected.to_rows() - table_is: Table = Table.from_rows(rows_is) - assert table_is == table_expected - - -def test_from_rows_invalid() -> None: - with pytest.raises(MissingDataError): - Table.from_rows([]) +def test_should_raise_error_if_mismatching_schema() -> None: + rows = [Row({"A": 1, "B": 2}), Row({"A": 2, "B": "a"})] + with pytest.raises(SchemaMismatchError, match=r"Failed because at least two schemas didn't match."): + Table.from_rows(rows) diff --git a/tests/safeds/data/tabular/containers/_table/test_get_column.py b/tests/safeds/data/tabular/containers/_table/test_get_column.py index 62fa2b7aa..ab160bc40 100644 --- a/tests/safeds/data/tabular/containers/_table/test_get_column.py +++ b/tests/safeds/data/tabular/containers/_table/test_get_column.py @@ -1,17 +1,27 @@ -import pandas as pd import pytest from safeds.data.tabular.containers import Column, Table -from safeds.data.tabular.exceptions import UnknownColumnNameError +from safeds.exceptions import UnknownColumnNameError -def test_get_column_valid() -> None: - table = Table.from_dict({"col1": ["col1_1"], "col2": ["col2_1"]}) - assert isinstance(table.get_column("col1"), Column) - assert table.get_column("col1")._data[0] == pd.Series(data=["col1_1"])[0] - assert table.get_column("col1")._data[0] == "col1_1" +@pytest.mark.parametrize( + ("table1", "expected"), + [ + (Table({"col1": ["col1_1"], "col2": ["col2_1"]}), Column("col1", ["col1_1"])), + ], + ids=["First column"], +) +def test_should_get_column(table1: Table, expected: Column) -> None: + assert table1.get_column("col1") == expected -def test_get_column_invalid() -> None: - table = Table.from_dict({"col1": ["col1_1"], "col2": ["col2_1"]}) - with pytest.raises(UnknownColumnNameError): +@pytest.mark.parametrize( + "table", + [ + (Table({"col1": ["col1_1"], "col2": ["col2_1"]})), + (Table()), + ], + ids=["no col3", "empty"], +) +def test_should_raise_error_if_column_name_unknown(table: Table) -> None: + with pytest.raises(UnknownColumnNameError, match=r"Could not find column\(s\) 'col3'"): table.get_column("col3") diff --git a/tests/safeds/data/tabular/containers/_table/test_get_column_names.py b/tests/safeds/data/tabular/containers/_table/test_get_column_names.py deleted file mode 100644 index 97ca7b010..000000000 --- a/tests/safeds/data/tabular/containers/_table/test_get_column_names.py +++ /dev/null @@ -1,11 +0,0 @@ -from safeds.data.tabular.containers import Table - - -def test_get_column_names() -> None: - table = Table.from_dict({"col1": [1], "col2": [1]}) - assert table.get_column_names() == ["col1", "col2"] - - -def test_get_column_names_empty() -> None: - table = Table([]) - assert table.get_column_names() == [] diff --git a/tests/safeds/data/tabular/containers/_table/test_get_row.py b/tests/safeds/data/tabular/containers/_table/test_get_row.py index d3dbd2d70..538157536 100644 --- a/tests/safeds/data/tabular/containers/_table/test_get_row.py +++ b/tests/safeds/data/tabular/containers/_table/test_get_row.py @@ -1,24 +1,40 @@ import pytest -from safeds.data.tabular.containers import Table -from safeds.data.tabular.exceptions import IndexOutOfBoundsError +from safeds.data.tabular.containers import Row, Table +from safeds.exceptions import IndexOutOfBoundsError -from tests.helpers import resolve_resource_path +@pytest.mark.parametrize( + ("table1", "expected"), + [ + (Table({"A": [1], "B": [2]}), Row({"A": 1, "B": 2})), + ], + ids=["table with one row"], +) +def test_should_get_row(table1: Table, expected: Row) -> None: + assert table1.get_row(0).schema == expected.schema + assert table1.get_row(0) == expected -def test_get_row() -> None: - table = Table.from_csv_file(resolve_resource_path("test_table_from_csv_file.csv")) - val = table.get_row(0) - assert val.get_value("A") == 1 - assert val.get_value("B") == 2 - -def test_get_row_negative_index() -> None: - table = Table.from_csv_file(resolve_resource_path("test_table_from_csv_file.csv")) +@pytest.mark.parametrize( + ("index", "table"), + [ + (-1, Table({"A": [1], "B": [2]})), + (5, Table({"A": [1], "B": [2]})), + (0, Table()), + ], + ids=["<0", "too high", "empty"], +) +def test_should_raise_error_if_index_out_of_bounds(index: int, table: Table) -> None: with pytest.raises(IndexOutOfBoundsError): - table.get_row(-1) + table.get_row(index) -def test_get_row_out_of_bounds_index() -> None: - table = Table.from_csv_file(resolve_resource_path("test_table_from_csv_file.csv")) - with pytest.raises(IndexOutOfBoundsError): - table.get_row(5) +@pytest.mark.parametrize( + ("index", "expected_error_message"), + [(-1, r"There is no element at index '-1'."), (5, r"There is no element at index '5'.")], + ids=["<0", "too high"], +) +def test_should_raise_error_if_index_out_of_bounds_error_message(index: int, expected_error_message: str) -> None: + table = Table({"A": [1], "B": [2]}) + with pytest.raises(IndexOutOfBoundsError, match=expected_error_message): + table.get_row(index) diff --git a/tests/safeds/data/tabular/containers/_table/test_group_by.py b/tests/safeds/data/tabular/containers/_table/test_group_by.py new file mode 100644 index 000000000..cfb38db36 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/test_group_by.py @@ -0,0 +1,35 @@ +from collections.abc import Callable + +import pytest +from safeds.data.tabular.containers import Table + + +@pytest.mark.parametrize( + ("table", "selector", "expected"), + [ + ( + Table({"col1": [1, 1, 2, 2, 3], "col2": ["a", "b", "c", "d", "e"]}), + lambda row: row["col1"], + { + 1: Table({"col1": [1, 1], "col2": ["a", "b"]}), + 2: Table({"col1": [2, 2], "col2": ["c", "d"]}), + 3: Table({"col1": [3], "col2": ["e"]}), + }, + ), + ( + Table({"col1": [1, 1, 2, 2, 3], "col2": ["a", "b", "c", "d", 2]}), + lambda row: row["col1"], + { + 1: Table({"col1": [1, 1], "col2": ["a", "b"]}), + 2: Table({"col1": [2, 2], "col2": ["c", "d"]}), + 3: Table({"col1": [3], "col2": [2]}), + }, + ), + (Table(), lambda row: row["col1"], {}), + (Table({"col1": [], "col2": []}), lambda row: row["col1"], {}), + ], + ids=["select by row1", "different types in column", "empty table", "table with no rows"], +) +def test_group_by(table: Table, selector: Callable, expected: dict) -> None: + out = table.group_by(selector) + assert out == expected diff --git a/tests/safeds/data/tabular/containers/_table/test_has_column.py b/tests/safeds/data/tabular/containers/_table/test_has_column.py index 3f89dc23b..90c6755b3 100644 --- a/tests/safeds/data/tabular/containers/_table/test_has_column.py +++ b/tests/safeds/data/tabular/containers/_table/test_has_column.py @@ -1,13 +1,11 @@ +import pytest from safeds.data.tabular.containers import Table -from tests.helpers import resolve_resource_path - -def test_has_column_positive() -> None: - table = Table.from_csv_file(resolve_resource_path("test_table_has_column.csv")) - assert table.has_column("A") - - -def test_has_column_negative() -> None: - table = Table.from_csv_file(resolve_resource_path("test_table_has_column.csv")) - assert not table.has_column("C") +@pytest.mark.parametrize( + ("table", "column", "expected"), + [(Table({"A": [1], "B": [2]}), "A", True), (Table({"A": [1], "B": [2]}), "C", False), (Table(), "C", False)], + ids=["has column", "doesn't have column", "empty"], +) +def test_should_return_if_column_is_in_table(table: Table, column: str, expected: bool) -> None: + assert table.has_column(column) == expected diff --git a/tests/safeds/data/tabular/containers/_table/test_init.py b/tests/safeds/data/tabular/containers/_table/test_init.py new file mode 100644 index 000000000..0f7895715 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/test_init.py @@ -0,0 +1,26 @@ +import pytest +from safeds.data.tabular.containers import Table +from safeds.data.tabular.typing import Integer, Schema +from safeds.exceptions import ColumnLengthMismatchError + + +@pytest.mark.parametrize( + ("table", "expected"), + [ + (Table(), Schema({})), + (Table({}), Schema({})), + (Table({"col1": [0]}), Schema({"col1": Integer()})), + ], + ids=[ + "empty", + "empty (explicit)", + "one column", + ], +) +def test_should_infer_the_schema(table: Table, expected: Schema) -> None: + assert table.schema == expected + + +def test_should_raise_error_if_columns_have_different_lengths() -> None: + with pytest.raises(ColumnLengthMismatchError, match=r"The length of at least one column differs: \na: 2\nb: 1"): + Table({"a": [1, 2], "b": [3]}) diff --git a/tests/safeds/data/tabular/containers/_table/test_inverse_transform_table.py b/tests/safeds/data/tabular/containers/_table/test_inverse_transform_table.py new file mode 100644 index 000000000..cc982c100 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/test_inverse_transform_table.py @@ -0,0 +1,120 @@ +import pytest +from safeds.data.tabular.containers import Table +from safeds.data.tabular.transformation import OneHotEncoder +from safeds.exceptions import TransformerNotFittedError + + +@pytest.mark.parametrize( + ("table_to_fit", "column_names", "table_to_transform"), + [ + ( + Table( + { + "a": [1.0, 0.0, 0.0, 0.0], + "b": ["a", "b", "b", "c"], + "c": [0.0, 0.0, 0.0, 1.0], + }, + ), + ["b"], + Table( + { + "a": [1.0, 0.0, 0.0, 0.0], + "b": ["a", "b", "b", "c"], + "c": [0.0, 0.0, 0.0, 1.0], + }, + ), + ), + ( + Table( + { + "a": [1.0, 0.0, 0.0, 0.0], + "b": ["a", "b", "b", "c"], + "c": [0.0, 0.0, 0.0, 1.0], + }, + ), + ["b"], + Table( + { + "c": [0.0, 0.0, 0.0, 1.0], + "b": ["a", "b", "b", "c"], + "a": [1.0, 0.0, 0.0, 0.0], + }, + ), + ), + ( + Table( + { + "a": [1.0, 0.0, 0.0, 0.0], + "b": ["a", "b", "b", "c"], + "bb": ["a", "b", "b", "c"], + }, + ), + ["b", "bb"], + Table( + { + "a": [1.0, 0.0, 0.0, 0.0], + "b": ["a", "b", "b", "c"], + "bb": ["a", "b", "b", "c"], + }, + ), + ), + ], + ids=[ + "same table to fit and transform", + "different tables to fit and transform", + "one column name is a prefix of another column name", + ], +) +def test_should_return_original_table( + table_to_fit: Table, + column_names: list[str], + table_to_transform: Table, +) -> None: + transformer = OneHotEncoder().fit(table_to_fit, column_names) + transformed_table = transformer.transform(table_to_transform) + + result = transformed_table.inverse_transform_table(transformer) + + # This checks whether the columns are in the same order + assert result.column_names == table_to_transform.column_names + # This is subsumed by the next assertion, but we get a better error message + assert result.schema == table_to_transform.schema + assert result == table_to_transform + + +def test_should_not_change_transformed_table() -> None: + table = Table( + { + "col1": ["a", "b", "b", "c"], + }, + ) + + transformer = OneHotEncoder().fit(table, None) + transformed_table = transformer.transform(table) + transformed_table.inverse_transform_table(transformer) + + expected = Table( + { + "col1__a": [1.0, 0.0, 0.0, 0.0], + "col1__b": [0.0, 1.0, 1.0, 0.0], + "col1__c": [0.0, 0.0, 0.0, 1.0], + }, + ) + + assert transformed_table.schema == expected.schema + assert transformed_table == expected + + +def test_should_raise_error_if_not_fitted() -> None: + table = Table( + { + "a": [1.0, 0.0, 0.0, 0.0], + "b": [0.0, 1.0, 1.0, 0.0], + "c": [0.0, 0.0, 0.0, 1.0], + }, + ) + + transformer = OneHotEncoder() + + with pytest.raises(TransformerNotFittedError, match=r"The transformer has not been fitted yet."): + table.inverse_transform_table(transformer) diff --git a/tests/safeds/data/tabular/containers/_table/test_keep_only_columns.py b/tests/safeds/data/tabular/containers/_table/test_keep_only_columns.py index c00653d6a..522e0e068 100644 --- a/tests/safeds/data/tabular/containers/_table/test_keep_only_columns.py +++ b/tests/safeds/data/tabular/containers/_table/test_keep_only_columns.py @@ -1,45 +1,54 @@ import pytest from safeds.data.tabular.containers import Table -from safeds.data.tabular.exceptions import UnknownColumnNameError +from safeds.exceptions import UnknownColumnNameError -class TestKeepOnlyColumns: - @pytest.mark.parametrize( - ("table", "column_names", "expected"), - [ - ( - Table.from_dict({"A": [1], "B": [2]}), - [], - Table.from_dict({}), - ), - ( - Table.from_dict({"A": [1], "B": [2]}), - ["A"], - Table.from_dict({"A": [1]}), - ), - ( - Table.from_dict({"A": [1], "B": [2]}), - ["B"], - Table.from_dict({"B": [2]}), - ), - ( - Table.from_dict({"A": [1], "B": [2]}), - ["A", "B"], - Table.from_dict({"A": [1], "B": [2]}), - ), - # Related to https://github.com/Safe-DS/Stdlib/issues/115 - ( - Table.from_dict({"A": [1], "B": [2], "C": [3]}), - ["C", "A"], - Table.from_dict({"C": [3], "A": [1]}), - ), - ], - ) - def test_should_keep_only_listed_columns(self, table: Table, column_names: list[str], expected: Table) -> None: - transformed_table = table.keep_only_columns(column_names) - assert transformed_table == expected +@pytest.mark.parametrize( + ("table", "column_names", "expected"), + [ + ( + Table({"A": [1], "B": [2]}), + [], + Table(), + ), + ( + Table({"A": [1], "B": [2]}), + ["A"], + Table({"A": [1]}), + ), + ( + Table({"A": [1], "B": [2]}), + ["B"], + Table({"B": [2]}), + ), + ( + Table({"A": [1], "B": [2]}), + ["A", "B"], + Table({"A": [1], "B": [2]}), + ), + # Related to https://github.com/Safe-DS/Stdlib/issues/115 + ( + Table({"A": [1], "B": [2], "C": [3]}), + ["C", "A"], + Table({"C": [3], "A": [1]}), + ), + ( + Table(), + [], + Table(), + ), + ], + ids=["No Column Name", "First Column", "Second Column", "All columns", "Last and first columns", "empty"], +) +def test_should_keep_only_listed_columns(table: Table, column_names: list[str], expected: Table) -> None: + transformed_table = table.keep_only_columns(column_names) + assert transformed_table.schema == expected.schema + assert transformed_table == expected + if len(column_names) == 0: + assert expected.number_of_rows == 0 - def test_should_raise_if_column_does_no_exist(self) -> None: - table = Table.from_dict({"A": [1], "B": [2]}) - with pytest.raises(UnknownColumnNameError): - table.keep_only_columns(["C"]) + +@pytest.mark.parametrize("table", [Table({"A": [1], "B": [2]}), Table()], ids=["table", "empty"]) +def test_should_raise_error_if_column_name_unknown(table: Table) -> None: + with pytest.raises(UnknownColumnNameError, match=r"Could not find column\(s\) 'C'"): + table.keep_only_columns(["C"]) diff --git a/tests/safeds/data/tabular/containers/_table/test_lineplot.py b/tests/safeds/data/tabular/containers/_table/test_lineplot.py deleted file mode 100644 index c315c7286..000000000 --- a/tests/safeds/data/tabular/containers/_table/test_lineplot.py +++ /dev/null @@ -1,17 +0,0 @@ -import _pytest -import matplotlib.pyplot as plt -import pytest -from safeds.data.tabular.containers import Table -from safeds.data.tabular.exceptions import UnknownColumnNameError - - -def test_lineplot(monkeypatch: _pytest.monkeypatch) -> None: - monkeypatch.setattr(plt, "show", lambda: None) - table = Table.from_dict({"A": [1, 2, 3], "B": [2, 4, 7]}) - table.lineplot("A", "B") - - -def test_lineplot_wrong_column_name() -> None: - table = Table.from_dict({"A": [1, 2, 3], "B": [2, 4, 7]}) - with pytest.raises(UnknownColumnNameError): - table.lineplot("C", "A") diff --git a/tests/safeds/data/tabular/containers/_table/test_number_of_columns.py b/tests/safeds/data/tabular/containers/_table/test_number_of_columns.py new file mode 100644 index 000000000..b5eb31320 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/test_number_of_columns.py @@ -0,0 +1,16 @@ +import pytest +from safeds.data.tabular.containers import Table + + +@pytest.mark.parametrize( + ("table", "expected"), + [ + (Table(), 0), + (Table({}), 0), + (Table({"col1": []}), 1), + (Table({"col1": [], "col2": []}), 2), + ], + ids=["empty", "empty 2", "a column", "2 columns"], +) +def test_should_return_number_of_columns(table: Table, expected: int) -> None: + assert table.number_of_columns == expected diff --git a/tests/safeds/data/tabular/containers/_table/test_number_of_rows.py b/tests/safeds/data/tabular/containers/_table/test_number_of_rows.py new file mode 100644 index 000000000..7a5ffe2f7 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/test_number_of_rows.py @@ -0,0 +1,16 @@ +import pytest +from safeds.data.tabular.containers import Table + + +@pytest.mark.parametrize( + ("table", "expected"), + [ + (Table(), 0), + (Table({}), 0), + (Table({"col1": [1]}), 1), + (Table({"col1": [1, 2]}), 2), + ], + ids=["empty", "empty 2", "a row", "2 rows"], +) +def test_should_return_number_of_rows(table: Table, expected: int) -> None: + assert table.number_of_rows == expected diff --git a/tests/safeds/data/tabular/containers/_table/test_plot_boxplots.py b/tests/safeds/data/tabular/containers/_table/test_plot_boxplots.py new file mode 100644 index 000000000..d8f615ef7 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/test_plot_boxplots.py @@ -0,0 +1,47 @@ +import pytest +from safeds.data.image.containers import Image +from safeds.data.tabular.containers import Table +from safeds.exceptions import NonNumericColumnError + +from tests.helpers import resolve_resource_path + + +@pytest.mark.parametrize( + ("table", "path"), + [ + (Table({"A": [1, 2, 3]}), "./image/snapshot_boxplots/one_column.png"), + ( + Table({"A": [1, 2, 3], "B": ["A", "A", "Bla"], "C": [True, True, False], "D": [1.0, 2.1, 4.5]}), + "./image/snapshot_boxplots/four_columns_some_non_numeric.png", + ), + ( + Table({"A": [1, 2, 3], "B": [1.0, 2.1, 4.5], "C": [1, 2, 3], "D": [1.0, 2.1, 4.5]}), + "./image/snapshot_boxplots/four_columns_all_numeric.png", + ), + ], + ids=["one column", "four columns (some non-numeric)", "four columns (all numeric)"], +) +def test_should_match_snapshot(table: Table, path: str) -> None: + current = table.plot_boxplots() + snapshot = Image.from_png_file(resolve_resource_path(path)) + + # Inlining the expression into the assert causes pytest to hang if the assertion fails when run from PyCharm. + assertion = snapshot._image.tobytes() == current._image.tobytes() + assert assertion + + +def test_should_raise_if_column_contains_non_numerical_values() -> None: + table = Table.from_dict({"A": ["1", "2", "3.5"], "B": ["0.2", "4", "77"]}) + with pytest.raises( + NonNumericColumnError, + match=( + r"Tried to do a numerical operation on one or multiple non-numerical columns: \nThis table contains only" + r" non-numerical columns." + ), + ): + table.plot_boxplots() + + +def test_should_fail_on_empty_table() -> None: + with pytest.raises(NonNumericColumnError): + Table().plot_boxplots() diff --git a/tests/safeds/data/tabular/containers/_table/test_plot_correlation_heatmap.py b/tests/safeds/data/tabular/containers/_table/test_plot_correlation_heatmap.py new file mode 100644 index 000000000..f87cfcba7 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/test_plot_correlation_heatmap.py @@ -0,0 +1,22 @@ +import pytest +from safeds.data.image.containers import Image +from safeds.data.tabular.containers import Table + +from tests.helpers import resolve_resource_path + + +@pytest.mark.parametrize( + ("table", "path"), + [ + (Table({"A": [1, 2, 3.5], "B": [0.2, 4, 77]}), "./image/snapshot_heatmap.png"), + (Table(), "./image/snapshot_empty_heatmap.png"), + ], + ids=["normal", "empty"], +) +def test_should_match_snapshot(table: Table, path: str) -> None: + current = table.plot_correlation_heatmap() + snapshot = Image.from_png_file(resolve_resource_path(path)) + + # Inlining the expression into the assert causes pytest to hang if the assertion fails when run from PyCharm. + assertion = snapshot._image.tobytes() == current._image.tobytes() + assert assertion diff --git a/tests/safeds/data/tabular/containers/_table/test_plot_histograms.py b/tests/safeds/data/tabular/containers/_table/test_plot_histograms.py new file mode 100644 index 000000000..edcdab313 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/test_plot_histograms.py @@ -0,0 +1,30 @@ +import pytest +from safeds.data.image.containers import Image +from safeds.data.tabular.containers import Table + +from tests.helpers import resolve_resource_path + + +@pytest.mark.parametrize( + ("table", "path"), + [ + (Table({"A": [1, 2, 3]}), "./image/snapshot_histograms/one_column.png"), + ( + Table({"A": [1, 2, 3], "B": ["A", "A", "Bla"], "C": [True, True, False], "D": [1.0, 2.1, 4.5]}), + "./image/snapshot_histograms/four_columns.png", + ), + ], + ids=["one column", "four columns"], +) +def test_should_match_snapshot(table: Table, path: str) -> None: + current = table.plot_histograms() + snapshot = Image.from_png_file(resolve_resource_path(path)) + + # Inlining the expression into the assert causes pytest to hang if the assertion fails when run from PyCharm. + assertion = snapshot._image.tobytes() == current._image.tobytes() + assert assertion + + +def test_should_fail_on_empty_table() -> None: + with pytest.raises(ZeroDivisionError): + Table().plot_histograms() diff --git a/tests/safeds/data/tabular/containers/_table/test_plot_lineplot.py b/tests/safeds/data/tabular/containers/_table/test_plot_lineplot.py new file mode 100644 index 000000000..93365ec76 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/test_plot_lineplot.py @@ -0,0 +1,46 @@ +import pytest +from safeds.data.image.containers import Image +from safeds.data.tabular.containers import Table +from safeds.exceptions import UnknownColumnNameError + +from tests.helpers import resolve_resource_path + + +def test_should_match_snapshot() -> None: + table = Table({"A": [1, 2, 3], "B": [2, 4, 7]}) + current = table.plot_lineplot("A", "B") + snapshot = Image.from_png_file(resolve_resource_path("./image/snapshot_lineplot.png")) + + # Inlining the expression into the assert causes pytest to hang if the assertion fails when run from PyCharm. + assertion = snapshot._image.tobytes() == current._image.tobytes() + assert assertion + + +@pytest.mark.parametrize( + ("table", "x", "y"), + [ + (Table({"A": [1, 2, 3], "B": [2, 4, 7]}), "C", "A"), + (Table({"A": [1, 2, 3], "B": [2, 4, 7]}), "A", "C"), + (Table(), "x", "y"), + ], + ids=["x column", "y column", "empty"], +) +def test_should_raise_if_column_does_not_exist(table: Table, x: str, y: str) -> None: + table = Table({"A": [1, 2, 3], "B": [2, 4, 7]}) + with pytest.raises(UnknownColumnNameError): + table.plot_lineplot(x, y) + + +@pytest.mark.parametrize( + ("x", "y", "error_message"), + [ + ("C", "A", r"Could not find column\(s\) 'C'"), + ("A", "C", r"Could not find column\(s\) 'C'"), + ("C", "D", r"Could not find column\(s\) 'C, D'"), + ], + ids=["x column", "y column", "x and y column"], +) +def test_should_raise_if_column_does_not_exist_error_message(x: str, y: str, error_message: str) -> None: + table = Table({"A": [1, 2, 3], "B": [2, 4, 7]}) + with pytest.raises(UnknownColumnNameError, match=error_message): + table.plot_lineplot(x, y) diff --git a/tests/safeds/data/tabular/containers/_table/test_plot_scatterplot.py b/tests/safeds/data/tabular/containers/_table/test_plot_scatterplot.py new file mode 100644 index 000000000..f07763fb8 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/test_plot_scatterplot.py @@ -0,0 +1,31 @@ +import pytest +from safeds.data.image.containers import Image +from safeds.data.tabular.containers import Table +from safeds.exceptions import UnknownColumnNameError + +from tests.helpers import resolve_resource_path + + +def test_should_match_snapshot() -> None: + table = Table({"A": [1, 2, 3], "B": [2, 4, 7]}) + current = table.plot_scatterplot("A", "B") + snapshot = Image.from_png_file(resolve_resource_path("./image/snapshot_scatterplot.png")) + + # Inlining the expression into the assert causes pytest to hang if the assertion fails when run from PyCharm. + assertion = snapshot._image.tobytes() == current._image.tobytes() + assert assertion + + +@pytest.mark.parametrize( + ("table", "col1", "col2", "error_message"), + [ + (Table({"A": [1, 2, 3], "B": [2, 4, 7]}), "C", "A", r"Could not find column\(s\) 'C'"), + (Table({"A": [1, 2, 3], "B": [2, 4, 7]}), "B", "C", r"Could not find column\(s\) 'C'"), + (Table({"A": [1, 2, 3], "B": [2, 4, 7]}), "C", "D", r"Could not find column\(s\) 'C, D'"), + (Table(), "C", "D", r"Could not find column\(s\) 'C, D'"), + ], + ids=["First argument doesn't exist", "Second argument doesn't exist", "Both arguments do not exist", "empty"], +) +def test_should_raise_if_column_does_not_exist(table: Table, col1: str, col2: str, error_message: str) -> None: + with pytest.raises(UnknownColumnNameError, match=error_message): + table.plot_scatterplot(col1, col2) diff --git a/tests/safeds/data/tabular/containers/_table/test_remove_columns.py b/tests/safeds/data/tabular/containers/_table/test_remove_columns.py index dd50c4f8e..ec29837a9 100644 --- a/tests/safeds/data/tabular/containers/_table/test_remove_columns.py +++ b/tests/safeds/data/tabular/containers/_table/test_remove_columns.py @@ -1,18 +1,26 @@ import pytest from safeds.data.tabular.containers import Table -from safeds.data.tabular.exceptions import UnknownColumnNameError +from safeds.exceptions import UnknownColumnNameError -from tests.helpers import resolve_resource_path +@pytest.mark.parametrize( + ("table", "expected", "columns"), + [ + (Table({"col1": [1, 2, 1], "col2": ["a", "b", "c"]}), Table({"col1": [1, 2, 1]}), ["col2"]), + (Table({"col1": [1, 2, 1], "col2": [1, 2, 4]}), Table(), ["col1", "col2"]), + (Table({"col1": [1, 2, 1], "col2": [1, 2, 4]}), Table({"col1": [1, 2, 1], "col2": [1, 2, 4]}), []), + (Table(), Table(), []), + ], + ids=["one column", "multiple columns", "no columns", "empty"], +) +def test_should_remove_table_columns(table: Table, expected: Table, columns: list[str]) -> None: + table = table.remove_columns(columns) + assert table.schema == expected.schema + assert table == expected + assert table.number_of_rows == expected.number_of_rows -def test_table_remove_columns() -> None: - table = Table.from_csv_file(resolve_resource_path("test_table_from_csv_file.csv")) - transformed_table = table.remove_columns(["A"]) - assert transformed_table.schema.has_column("B") - assert not transformed_table.schema.has_column("A") - -def test_table_remove_columns_warning() -> None: - table = Table.from_csv_file(resolve_resource_path("test_table_from_csv_file.csv")) - with pytest.raises(UnknownColumnNameError): +@pytest.mark.parametrize("table", [Table({"A": [1], "B": [2]}), Table()], ids=["normal", "empty"]) +def test_should_raise_if_column_not_found(table: Table) -> None: + with pytest.raises(UnknownColumnNameError, match=r"Could not find column\(s\) 'C'"): table.remove_columns(["C"]) diff --git a/tests/safeds/data/tabular/containers/_table/test_remove_columns_with_missing_values.py b/tests/safeds/data/tabular/containers/_table/test_remove_columns_with_missing_values.py index 94b82f925..f0e4d9b7e 100644 --- a/tests/safeds/data/tabular/containers/_table/test_remove_columns_with_missing_values.py +++ b/tests/safeds/data/tabular/containers/_table/test_remove_columns_with_missing_values.py @@ -1,21 +1,33 @@ +import pytest from safeds.data.tabular.containers import Table -from safeds.data.tabular.typing import RealNumber, Schema -def test_remove_columns_with_missing_values_valid() -> None: - table = Table.from_dict( - { - "col1": [None, None, None, None], - "col2": [1, 2, 3, None], - "col3": [1, 2, 3, 4], - "col4": [2, 3, 1, 4], - }, - ) +@pytest.mark.parametrize( + ("table", "expected"), + [ + ( + ( + Table( + { + "col1": [None, None, None, None], + "col2": [1, 2, 3, None], + "col3": [1, 2, 3, 4], + "col4": [2, 3, 1, 4], + }, + ), + Table( + { + "col3": [1, 2, 3, 4], + "col4": [2, 3, 1, 4], + }, + ), + ) + ), + (Table({}), Table({})), + ], + ids=["some missing values", "empty"], +) +def test_should_remove_columns_with_missing_values(table: Table, expected: Table) -> None: updated_table = table.remove_columns_with_missing_values() - assert updated_table.get_column_names() == ["col3", "col4"] - - -def test_remove_columns_with_missing_values_empty() -> None: - table = Table([], Schema({"col1": RealNumber()})) - updated_table = table.remove_columns_with_missing_values() - assert updated_table.get_column_names() == ["col1"] + assert updated_table.schema == expected.schema + assert updated_table == expected diff --git a/tests/safeds/data/tabular/containers/_table/test_remove_columns_with_non_numerical_values.py b/tests/safeds/data/tabular/containers/_table/test_remove_columns_with_non_numerical_values.py index c9c6ccb81..4fb663e00 100644 --- a/tests/safeds/data/tabular/containers/_table/test_remove_columns_with_non_numerical_values.py +++ b/tests/safeds/data/tabular/containers/_table/test_remove_columns_with_non_numerical_values.py @@ -1,21 +1,28 @@ +import pytest from safeds.data.tabular.containers import Table -from safeds.data.tabular.typing import RealNumber, Schema -def test_remove_columns_with_non_numerical_values_valid() -> None: - table = Table.from_dict( - { - "col1": ["A", "B", "C", "A"], - "col2": ["Test1", "Test1", "Test3", "Test1"], - "col3": [1, 2, 3, 4], - "col4": [2, 3, 1, 4], - }, - ) +@pytest.mark.parametrize( + ("table", "expected"), + [ + ( + Table( + { + "col1": ["text", "text", "word", "word"], + "col3": [2, 3, 1, 4], + }, + ), + Table( + { + "col3": [2, 3, 1, 4], + }, + ), + ), + (Table({}), Table({})), + ], + ids=["numerical values", "empty"], +) +def test_should_remove_columns_with_non_numerical_values(table: Table, expected: Table) -> None: updated_table = table.remove_columns_with_non_numerical_values() - assert updated_table.get_column_names() == ["col3", "col4"] - - -def test_remove_columns_with_non_numerical_values_empty() -> None: - table = Table([], Schema({"col1": RealNumber()})) - updated_table = table.remove_columns_with_non_numerical_values() - assert updated_table.get_column_names() == ["col1"] + assert updated_table.schema == expected.schema + assert updated_table == expected diff --git a/tests/safeds/data/tabular/containers/_table/test_remove_duplicate_rows.py b/tests/safeds/data/tabular/containers/_table/test_remove_duplicate_rows.py index 82289d526..3a3470cd8 100644 --- a/tests/safeds/data/tabular/containers/_table/test_remove_duplicate_rows.py +++ b/tests/safeds/data/tabular/containers/_table/test_remove_duplicate_rows.py @@ -1,18 +1,24 @@ import pytest from safeds.data.tabular.containers import Table -from tests.helpers import resolve_resource_path - @pytest.mark.parametrize( - "path", + ("table", "expected"), [ - "test_table_duplicate_rows_duplicates.csv", - "test_table_duplicate_rows_no_duplicates.csv", + ( + Table( + { + "A": [1, 1, 1, 4], + "B": [2, 2, 2, 5], + }, + ), + Table({"A": [1, 4], "B": [2, 5]}), + ), + (Table(), Table()), ], + ids=["duplicate rows", "empty"], ) -def test_remove_duplicate_rows(path: str) -> None: - expected_table = Table.from_dict({"A": [1, 4], "B": [2, 5]}) - table = Table.from_csv_file(resolve_resource_path(path)) +def test_should_remove_duplicate_rows(table: Table, expected: Table) -> None: result_table = table.remove_duplicate_rows() - assert expected_table == result_table + assert result_table.schema == expected.schema + assert result_table == expected diff --git a/tests/safeds/data/tabular/containers/_table/test_remove_rows_with_missing_values.py b/tests/safeds/data/tabular/containers/_table/test_remove_rows_with_missing_values.py index b6d665cd5..43e91f919 100644 --- a/tests/safeds/data/tabular/containers/_table/test_remove_rows_with_missing_values.py +++ b/tests/safeds/data/tabular/containers/_table/test_remove_rows_with_missing_values.py @@ -1,21 +1,29 @@ +import pytest from safeds.data.tabular.containers import Table -from safeds.data.tabular.typing import RealNumber, Schema -def test_remove_rows_with_missing_values_valid() -> None: - table = Table.from_dict( - { - "col1": [None, None, "C", "A"], - "col2": [None, "Test1", "Test3", "Test1"], - "col3": [None, 2, 3, 4], - "col4": [None, 3, 1, 4], - }, - ) +@pytest.mark.parametrize( + ("table", "expected"), + [ + ( + Table( + { + "col1": [None, None, "C", "A"], + "col2": [None, "2", "3", "4"], + }, + ), + Table( + { + "col1": ["C", "A"], + "col2": ["3", "4"], + }, + ), + ), + (Table({}), Table({})), + ], + ids=["some missing values", "empty"], +) +def test_should_remove_rows_with_missing_values(table: Table, expected: Table) -> None: updated_table = table.remove_rows_with_missing_values() - assert updated_table.count_rows() == 2 - - -def test_remove_rows_with_missing_values_empty() -> None: - table = Table([], Schema({"col1": RealNumber()})) - updated_table = table.remove_rows_with_missing_values() - assert updated_table.get_column_names() == ["col1"] + assert updated_table.schema == expected.schema + assert updated_table == expected diff --git a/tests/safeds/data/tabular/containers/_table/test_remove_rows_with_outliers.py b/tests/safeds/data/tabular/containers/_table/test_remove_rows_with_outliers.py index dcc7f2bf0..9ebf37167 100644 --- a/tests/safeds/data/tabular/containers/_table/test_remove_rows_with_outliers.py +++ b/tests/safeds/data/tabular/containers/_table/test_remove_rows_with_outliers.py @@ -1,58 +1,266 @@ +import pytest from safeds.data.tabular.containers import Table -from safeds.data.tabular.typing import RealNumber, Schema -def test_remove_rows_with_outliers_no_outliers() -> None: - table = Table.from_dict( - { - "col1": ["A", "B", "C"], - "col2": [1.0, 2.0, 3.0], - "col3": [2, 3, 1], - }, - ) - names = table.get_column_names() - result = table.remove_rows_with_outliers() - assert result.count_rows() == 3 - assert result.count_columns() == 3 - assert names == table.get_column_names() - - -def test_remove_rows_with_outliers_with_outliers() -> None: - input_ = Table.from_dict( - { - "col1": [ - "A", - "B", - "C", - "outlier", - "a", - "a", - "a", - "a", - "a", - "a", - "a", - "a", - ], - "col2": [1.0, 2.0, 3.0, 4.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, None], - "col3": [2, 3, 1, 1_000_000_000, 1, 1, 1, 1, 1, 1, 1, 1], - }, - ) - result = input_.remove_rows_with_outliers() - - expected = Table.from_dict( - { - "col1": ["A", "B", "C", "a", "a", "a", "a", "a", "a", "a", "a"], - "col2": [1.0, 2.0, 3.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, None], - "col3": [2, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1], - }, - ) - - assert result == expected - - -def test_remove_rows_with_outliers_no_rows() -> None: - table = Table([], Schema({"col1": RealNumber()})) - result = table.remove_rows_with_outliers() - assert result.count_rows() == 0 - assert result.count_columns() == 1 +@pytest.mark.parametrize( + ("table", "expected"), + [ + ( + Table( + { + "col1": ["A", "B", "C"], + "col2": [1.0, 2.0, 3.0], + "col3": [2, 3, 1], + }, + ), + Table( + { + "col1": ["A", "B", "C"], + "col2": [1.0, 2.0, 3.0], + "col3": [2, 3, 1], + }, + ), + ), + ( + Table( + { + "col1": [ + "A", + "B", + "A", + "outlier", + "a", + "a", + "a", + "a", + "a", + "a", + "a", + "a", + ], + "col2": [1.0, 2.0, 3.0, 4.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, None], + "col3": [2, 3, 1, 1_000_000_000, 1, 1, 1, 1, 1, 1, 1, 1], + }, + ), + Table( + { + "col1": [ + "A", + "B", + "A", + "a", + "a", + "a", + "a", + "a", + "a", + "a", + "a", + ], + "col2": [1.0, 2.0, 3.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, None], + "col3": [2, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1], + }, + ), + ), + ( + Table( + { + "col1": [ + "A", + "B", + "A", + "outlier_col3", + "a", + "a", + "a", + "a", + "a", + "a", + "outlier_col2", + "a", + ], + "col2": [1.0, 2.0, 3.0, 4.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, -1000.0, None], + "col3": [2, 3, 1, 1_000_000_000, 1, 1, 1, 1, 1, 1, 1, 1], + }, + ), + Table( + { + "col1": [ + "A", + "B", + "A", + "a", + "a", + "a", + "a", + "a", + "a", + "a", + ], + "col2": [1.0, 2.0, 3.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, None], + "col3": [2, 3, 1, 1, 1, 1, 1, 1, 1, 1], + }, + ), + ), + ( + Table( + { + "col1": [ + "A", + "B", + "A", + "positive_outlier", + "a", + "a", + "a", + "a", + "a", + "a", + "a", + "a", + "negative_outlier", + "a", + "a", + "a", + "a", + "a", + "a", + "a", + "a", + "a", + "a", + ], + "col2": [ + 1.0, + 2.0, + 3.0, + 4.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + None, + 1.0, + 2.0, + 1.0, + 4.0, + 1.0, + 3.0, + 1.0, + 2.0, + 1.0, + 4.0, + 1.0, + ], + "col3": [ + 2, + 3, + 1, + 1_000_000_000_000, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + -1_000_000_000_000, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + ], + }, + ), + Table( + { + "col1": [ + "A", + "B", + "A", + "a", + "a", + "a", + "a", + "a", + "a", + "a", + "a", + "a", + "a", + "a", + "a", + "a", + "a", + "a", + "a", + "a", + "a", + ], + "col2": [ + 1.0, + 2.0, + 3.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + None, + 2.0, + 1.0, + 4.0, + 1.0, + 3.0, + 1.0, + 2.0, + 1.0, + 4.0, + 1.0, + ], + "col3": [2, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], + }, + ), + ), + ( + Table( + { + "col1": [], + "col2": [], + }, + ), + Table( + { + "col1": [], + "col2": [], + }, + ), + ), + (Table(), Table()), + ], + ids=[ + "no outliers", + "one outlier", + "outliers in two different columns", + "multiple outliers in one column", + "no rows", + "empty", + ], +) +def test_should_remove_rows_with_outliers(table: Table, expected: Table) -> None: + updated_table = table.remove_rows_with_outliers() + assert updated_table.schema == expected.schema + assert updated_table.number_of_rows == expected.number_of_rows + assert updated_table == expected diff --git a/tests/safeds/data/tabular/containers/_table/test_rename.py b/tests/safeds/data/tabular/containers/_table/test_rename.py index 7723ba3db..0ee0fe152 100644 --- a/tests/safeds/data/tabular/containers/_table/test_rename.py +++ b/tests/safeds/data/tabular/containers/_table/test_rename.py @@ -1,31 +1,27 @@ import pytest from safeds.data.tabular.containers import Table -from safeds.data.tabular.exceptions import DuplicateColumnNameError, UnknownColumnNameError - -from tests.helpers import resolve_resource_path +from safeds.exceptions import DuplicateColumnNameError, UnknownColumnNameError @pytest.mark.parametrize( ("name_from", "name_to", "column_one", "column_two"), [("A", "D", "D", "B"), ("A", "A", "A", "B")], + ids=["column renamed", "column not renamed"], ) -def test_rename_valid(name_from: str, name_to: str, column_one: str, column_two: str) -> None: - table: Table = Table.from_csv_file(resolve_resource_path("test_table_from_csv_file.csv")) +def test_should_rename_column(name_from: str, name_to: str, column_one: str, column_two: str) -> None: + table: Table = Table({"A": [1], "B": [2]}) renamed_table = table.rename_column(name_from, name_to) - assert renamed_table.schema.has_column(column_one) - assert renamed_table.schema.has_column(column_two) - assert renamed_table.count_columns() == 2 + assert renamed_table.schema.column_names == [column_one, column_two] + assert renamed_table.column_names == [column_one, column_two] -@pytest.mark.parametrize( - ("name_from", "name_to", "error"), - [ - ("C", "D", UnknownColumnNameError), - ("A", "B", DuplicateColumnNameError), - ("D", "D", UnknownColumnNameError), - ], -) -def test_rename_invalid(name_from: str, name_to: str, error: Exception) -> None: - table: Table = Table.from_csv_file(resolve_resource_path("test_table_from_csv_file.csv")) - with pytest.raises(error): - table.rename_column(name_from, name_to) +@pytest.mark.parametrize("table", [Table({"A": [1], "B": [2]}), Table()], ids=["normal", "empty"]) +def test_should_raise_if_old_column_does_not_exist(table: Table) -> None: + with pytest.raises(UnknownColumnNameError, match=r"Could not find column\(s\) 'C'"): + table.rename_column("C", "D") + + +def test_should_raise_if_new_column_exists_already() -> None: + table: Table = Table({"A": [1], "B": [2]}) + with pytest.raises(DuplicateColumnNameError, match=r"Column 'B' already exists."): + table.rename_column("A", "B") diff --git a/tests/safeds/data/tabular/containers/_table/test_replace_column.py b/tests/safeds/data/tabular/containers/_table/test_replace_column.py index 5cb9f76fe..e19ecb3cb 100644 --- a/tests/safeds/data/tabular/containers/_table/test_replace_column.py +++ b/tests/safeds/data/tabular/containers/_table/test_replace_column.py @@ -1,48 +1,115 @@ import pytest from safeds.data.tabular.containers import Column, Table -from safeds.data.tabular.exceptions import ( +from safeds.exceptions import ( ColumnSizeError, DuplicateColumnNameError, UnknownColumnNameError, ) -from tests.helpers import resolve_resource_path - @pytest.mark.parametrize( - ("column_name", "path"), + ("table", "column_name", "columns", "expected"), [ - ("C", "test_table_replace_column_output_different_name.csv"), - ("D", "test_table_replace_column_output_same_name.csv"), + ( + Table( + { + "A": [1, 2, 3], + "B": [4, 5, 6], + "C": ["a", "b", "c"], + }, + ), + "B", + [Column("B", ["d", "e", "f"]), Column("D", [3, 4, 5])], + Table( + { + "A": [1, 2, 3], + "B": ["d", "e", "f"], + "D": [3, 4, 5], + "C": ["a", "b", "c"], + }, + ), + ), + ( + Table( + { + "A": [1, 2, 3], + "B": [4, 5, 6], + "C": ["a", "b", "c"], + }, + ), + "C", + [Column("D", ["d", "e", "f"])], + Table( + { + "A": [1, 2, 3], + "B": [4, 5, 6], + "D": ["d", "e", "f"], + }, + ), + ), + ( + Table( + { + "A": [1, 2, 3], + "B": [4, 5, 6], + "C": ["a", "b", "c"], + }, + ), + "C", + [], + Table( + { + "A": [1, 2, 3], + "B": [4, 5, 6], + }, + ), + ), ], + ids=["multiple Columns", "one Column", "empty"], ) -def test_replace_valid(column_name: str, path: str) -> None: - input_table: Table = Table.from_csv_file(resolve_resource_path("test_table_replace_column_input.csv")) - expected: Table = Table.from_csv_file(resolve_resource_path(path)) - - column = Column(column_name, ["d", "e", "f"]) - - result = input_table.replace_column("C", column) - +def test_should_replace_column(table: Table, column_name: str, columns: list[Column], expected: Table) -> None: + result = table.replace_column(column_name, columns) + assert result._schema == expected._schema assert result == expected @pytest.mark.parametrize( - ("old_column_name", "column_values", "column_name", "error"), + ("old_column_name", "column", "error", "error_message"), [ - ("D", ["d", "e", "f"], "C", UnknownColumnNameError), - ("C", ["d", "e", "f"], "B", DuplicateColumnNameError), - ("C", ["d", "e"], "D", ColumnSizeError), + ("D", [Column("C", ["d", "e", "f"])], UnknownColumnNameError, r"Could not find column\(s\) 'D'"), + ( + "C", + [Column("B", ["d", "e", "f"]), Column("D", [3, 2, 1])], + DuplicateColumnNameError, + r"Column 'B' already exists.", + ), + ( + "C", + [Column("D", [7, 8]), Column("E", ["c", "b"])], + ColumnSizeError, + r"Expected a column of size 3 but got column of size 2.", + ), ], + ids=["UnknownColumnNameError", "DuplicateColumnNameError", "ColumnSizeError"], ) -def test_replace_invalid( +def test_should_raise_error( old_column_name: str, - column_values: list[str], - column_name: str, + column: list[Column], error: type[Exception], + error_message: str, ) -> None: - input_table: Table = Table.from_csv_file(resolve_resource_path("test_table_replace_column_input.csv")) - column = Column(column_name, column_values) + input_table: Table = Table( + { + "A": [1, 2, 3], + "B": [4, 5, 6], + "C": ["a", "b", "c"], + }, + ) - with pytest.raises(error): + with pytest.raises(error, match=error_message): input_table.replace_column(old_column_name, column) + + +def test_should_fail_on_empty_table() -> None: + with pytest.raises(UnknownColumnNameError): + Table().replace_column("col", [Column("a", [1, 2])]) diff --git a/tests/safeds/data/tabular/containers/_table/test_repr.py b/tests/safeds/data/tabular/containers/_table/test_repr.py new file mode 100644 index 000000000..fee0c1d84 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/test_repr.py @@ -0,0 +1,19 @@ +import pytest +from safeds.data.tabular.containers import Table + + +@pytest.mark.parametrize( + ("table", "expected"), + [ + (Table(), "Empty DataFrame\nColumns: []\nIndex: []"), + (Table({"col1": [0]}), " col1\n0 0"), + (Table({"col1": [0, "1"], "col2": ["a", "b"]}), " col1 col2\n0 0 a\n1 1 b"), + ], + ids=[ + "empty table", + "table with one row", + "table with multiple rows", + ], +) +def test_should_return_a_string_representation(table: Table, expected: str) -> None: + assert repr(table) == expected diff --git a/tests/safeds/data/tabular/containers/_table/test_repr_html.py b/tests/safeds/data/tabular/containers/_table/test_repr_html.py new file mode 100644 index 000000000..8b25de38c --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/test_repr_html.py @@ -0,0 +1,53 @@ +import re + +import pytest +from safeds.data.tabular.containers import Table + + +@pytest.mark.parametrize( + "table", + [ + Table({}), + Table({"a": [1, 2], "b": [3, 4]}), + ], + ids=[ + "empty", + "non-empty", + ], +) +def test_should_contain_table_element(table: Table) -> None: + pattern = r".*?" + assert re.search(pattern, table._repr_html_(), flags=re.S) is not None + + +@pytest.mark.parametrize( + "table", + [ + Table({}), + Table({"a": [1, 2], "b": [3, 4]}), + ], + ids=[ + "empty", + "non-empty", + ], +) +def test_should_contain_th_element_for_each_column_name(table: Table) -> None: + for column_name in table.column_names: + assert f"{column_name}" in table._repr_html_() + + +@pytest.mark.parametrize( + "table", + [ + Table({}), + Table({"a": [1, 2], "b": [3, 4]}), + ], + ids=[ + "empty", + "non-empty", + ], +) +def test_should_contain_td_element_for_each_value(table: Table) -> None: + for column in table.to_columns(): + for value in column: + assert f"{value}" in table._repr_html_() diff --git a/tests/safeds/data/tabular/containers/_table/test_scatterplot.py b/tests/safeds/data/tabular/containers/_table/test_scatterplot.py deleted file mode 100644 index 3879db603..000000000 --- a/tests/safeds/data/tabular/containers/_table/test_scatterplot.py +++ /dev/null @@ -1,17 +0,0 @@ -import _pytest -import matplotlib.pyplot as plt -import pytest -from safeds.data.tabular.containers import Table -from safeds.data.tabular.exceptions import UnknownColumnNameError - - -def test_scatterplot(monkeypatch: _pytest.monkeypatch) -> None: - monkeypatch.setattr(plt, "show", lambda: None) - table = Table.from_dict({"A": [1, 2, 3], "B": [2, 4, 7]}) - table.scatterplot("A", "B") - - -def test_scatterplot_wrong_column_name() -> None: - table = Table.from_dict({"A": [1, 2, 3], "B": [2, 4, 7]}) - with pytest.raises(UnknownColumnNameError): - table.scatterplot("C", "A") diff --git a/tests/safeds/data/tabular/containers/_table/test_shuffle_rows.py b/tests/safeds/data/tabular/containers/_table/test_shuffle_rows.py index 15aed774b..2f11b852c 100644 --- a/tests/safeds/data/tabular/containers/_table/test_shuffle_rows.py +++ b/tests/safeds/data/tabular/containers/_table/test_shuffle_rows.py @@ -1,7 +1,13 @@ +import pytest from safeds.data.tabular.containers import Table -def test_shuffle_rows_valid() -> None: - table = Table.from_dict({"col1": [1], "col2": [1]}) +@pytest.mark.parametrize( + "table", + [(Table({"col1": [1], "col2": [1]})), Table()], + ids=["Table with identical values in rows", "empty"], +) +def test_should_shuffle_rows(table: Table) -> None: result_table = table.shuffle_rows() + assert table.schema == result_table.schema assert table == result_table diff --git a/tests/safeds/data/tabular/containers/_table/test_slice_rows.py b/tests/safeds/data/tabular/containers/_table/test_slice_rows.py index e35bcd04c..b79440100 100644 --- a/tests/safeds/data/tabular/containers/_table/test_slice_rows.py +++ b/tests/safeds/data/tabular/containers/_table/test_slice_rows.py @@ -1,31 +1,52 @@ import pytest +from _pytest.python_api import raises from safeds.data.tabular.containers import Table +from safeds.exceptions import IndexOutOfBoundsError -def test_slice_rows_valid() -> None: - table = Table.from_dict({"col1": [1, 2, 1], "col2": [1, 2, 4]}) - test_table = Table.from_dict({"col1": [1, 2], "col2": [1, 2]}) - second_test_table = Table.from_dict({"col1": [1, 1], "col2": [1, 4]}) +@pytest.mark.parametrize( + ("table", "test_table", "second_test_table"), + [ + ( + Table({"col1": [1, 2, 1], "col2": [1, 2, 4]}), + Table({"col1": [1, 2], "col2": [1, 2]}), + Table({"col1": [1, 1], "col2": [1, 4]}), + ), + ], + ids=["Table with three rows"], +) +def test_should_slice_rows(table: Table, test_table: Table, second_test_table: Table) -> None: new_table = table.slice_rows(0, 2, 1) second_new_table = table.slice_rows(0, 3, 2) third_new_table = table.slice_rows() + assert new_table.schema == test_table.schema assert new_table == test_table + assert second_new_table.schema == second_test_table.schema assert second_new_table == second_test_table + assert third_new_table.schema == table.schema assert third_new_table == table @pytest.mark.parametrize( - ("start", "end", "step"), + ("start", "end", "step", "error_message"), [ - (3, 2, 1), - (4, 0, 1), - (0, 4, 1), - (-4, 0, 1), - (0, -4, 1), + (3, 2, 1, r"There is no element in the range \[3, 2\]"), + (4, 0, 1, r"There is no element in the range \[4, 0\]"), + (0, 4, 1, r"There is no element at index '4'"), + (-4, 0, 1, r"There is no element at index '-4'"), + (0, -4, 1, r"There is no element in the range \[0, -4\]"), ], + ids=["Start > End", "Start > Length", "End > Length", "Start < 0", "End < 0"], ) -def test_slice_rows_invalid(start: int, end: int, step: int) -> None: - table = Table.from_dict({"col1": [1, 2, 1], "col2": [1, 2, 4]}) +def test_should_raise_if_index_out_of_bounds(start: int, end: int, step: int, error_message: str) -> None: + table = Table({"col1": [1, 2, 1], "col2": [1, 2, 4]}) - with pytest.raises(ValueError, match="The given index is out of bounds"): + with raises(IndexOutOfBoundsError, match=error_message): table.slice_rows(start, end, step) + + +def test_should_raise_if_index_out_of_bounds_on_empty() -> None: + table = Table() + + with pytest.raises(IndexOutOfBoundsError, match="There is no element at index '2'"): + table.slice_rows(2, 5, 1) diff --git a/tests/safeds/data/tabular/containers/_table/test_sort_columns.py b/tests/safeds/data/tabular/containers/_table/test_sort_columns.py index 4b742ce8c..7fefc80b7 100644 --- a/tests/safeds/data/tabular/containers/_table/test_sort_columns.py +++ b/tests/safeds/data/tabular/containers/_table/test_sort_columns.py @@ -16,15 +16,22 @@ 0, ), ], + ids=["no query", "with query"], ) -def test_sort_columns_valid(query: Callable[[Column, Column], int], col1: int, col2: int, col3: int, col4: int) -> None: +def test_should_return_sorted_table( + query: Callable[[Column, Column], int], + col1: int, + col2: int, + col3: int, + col4: int, +) -> None: columns = [ Column("col1", ["A", "B", "C", "A", "D"]), Column("col2", ["Test1", "Test1", "Test3", "Test1", "Test4"]), Column("col3", [1, 2, 3, 4, 5]), Column("col4", [2, 3, 1, 4, 6]), ] - table1 = Table.from_dict( + table1 = Table( { "col2": ["Test1", "Test1", "Test3", "Test1", "Test4"], "col3": [1, 2, 3, 4, 5], @@ -41,5 +48,10 @@ def test_sort_columns_valid(query: Callable[[Column, Column], int], col1: int, c assert table_sorted_columns[1] == columns[col2] assert table_sorted_columns[2] == columns[col3] assert table_sorted_columns[3] == columns[col4] - assert table_sorted.count_columns() == 4 - assert table_sorted.count_rows() == table1.count_rows() + assert table_sorted.number_of_columns == 4 + assert table_sorted.number_of_rows == table1.number_of_rows + assert table_sorted.schema == table1.schema + + +def test_should_not_sort_anything_on_empty_table() -> None: + assert Table() == Table().sort_columns() diff --git a/tests/safeds/data/tabular/containers/_table/test_sort_rows.py b/tests/safeds/data/tabular/containers/_table/test_sort_rows.py index 32115a883..9772dc112 100644 --- a/tests/safeds/data/tabular/containers/_table/test_sort_rows.py +++ b/tests/safeds/data/tabular/containers/_table/test_sort_rows.py @@ -4,42 +4,52 @@ from safeds.data.tabular.containers import Row, Table -class TestSortRows: - @pytest.mark.parametrize( - ("table", "comparator", "expected"), - [ - # Check that it works with an empty table when https://github.com/Safe-DS/Stdlib/issues/75 is fixed. - ( - Table.from_dict({"col1": [3, 2, 1]}), - lambda row1, row2: row1["col1"] - row2["col1"], - Table.from_dict({"col1": [1, 2, 3]}), - ), - ], - ) - def test_should_return_sorted_table( - self, - table: Table, - comparator: Callable[[Row, Row], int], - expected: Table, - ) -> None: - assert table.sort_rows(comparator) == expected +@pytest.mark.parametrize( + ("table", "comparator", "expected"), + [ + ( + Table(), + lambda row1, row2: row1["col1"] - row2["col1"], + Table(), + ), + ( + Table({"col1": [3, 2, 1]}), + lambda row1, row2: row1["col1"] - row2["col1"], + Table({"col1": [1, 2, 3]}), + ), + ], + ids=["empty", "3 rows"], +) +def test_should_return_sorted_table( + table: Table, + comparator: Callable[[Row, Row], int], + expected: Table, +) -> None: + assert table.sort_rows(comparator).schema == expected.schema + assert table.sort_rows(comparator) == expected - @pytest.mark.parametrize( - ("table", "comparator", "expected"), - [ - # Check that it works with an empty table when https://github.com/Safe-DS/Stdlib/issues/75 is fixed. - ( - Table.from_dict({"col1": [3, 2, 1]}), - lambda row1, row2: row1["col1"] - row2["col1"], - Table.from_dict({"col1": [3, 2, 1]}), - ), - ], - ) - def test_should_not_modify_original_table( - self, - table: Table, - comparator: Callable[[Row, Row], int], - expected: Table, - ) -> None: - table.sort_rows(comparator) - assert table == expected + +@pytest.mark.parametrize( + ("table", "comparator", "expected"), + [ + ( + Table(), + lambda row1, row2: row1["col1"] - row2["col1"], + Table(), + ), + ( + Table({"col1": [3, 2, 1]}), + lambda row1, row2: row1["col1"] - row2["col1"], + Table({"col1": [3, 2, 1]}), + ), + ], + ids=["empty", "3 rows"], +) +def test_should_not_modify_original_table( + table: Table, + comparator: Callable[[Row, Row], int], + expected: Table, +) -> None: + table.sort_rows(comparator) + assert table.schema == expected.schema + assert table == expected diff --git a/tests/safeds/data/tabular/containers/_table/test_split.py b/tests/safeds/data/tabular/containers/_table/test_split.py index 3715958cc..36789a346 100644 --- a/tests/safeds/data/tabular/containers/_table/test_split.py +++ b/tests/safeds/data/tabular/containers/_table/test_split.py @@ -1,27 +1,61 @@ +import pandas as pd import pytest from safeds.data.tabular.containers import Table +from safeds.data.tabular.typing import Integer, Schema -def test_split_valid() -> None: - table = Table.from_dict({"col1": [1, 2, 1], "col2": [1, 2, 4]}) - result_train_table = Table.from_dict({"col1": [1, 2], "col2": [1, 2]}) - result_test_table = Table.from_dict({"col1": [1], "col2": [4]}) - train_table, test_table = table.split(2 / 3) +@pytest.mark.parametrize( + ("table", "result_train_table", "result_test_table", "percentage_in_first"), + [ + ( + Table({"col1": [1, 2, 1], "col2": [1, 2, 4]}), + Table({"col1": [1, 2], "col2": [1, 2]}), + Table({"col1": [1], "col2": [4]}), + 2 / 3, + ), + ( + Table({"col1": [1, 2, 1], "col2": [1, 2, 4]}), + Table._from_pandas_dataframe(pd.DataFrame(), Schema({"col1": Integer(), "col2": Integer()})), + Table({"col1": [1, 2, 1], "col2": [1, 2, 4]}), + 0, + ), + ( + Table({"col1": [1, 2, 1], "col2": [1, 2, 4]}), + Table({"col1": [1, 2, 1], "col2": [1, 2, 4]}), + Table._from_pandas_dataframe(pd.DataFrame(), Schema({"col1": Integer(), "col2": Integer()})), + 1, + ), + ], + ids=["2/3%", "0%", "100%"], +) +def test_should_split_table( + table: Table, + result_test_table: Table, + result_train_table: Table, + percentage_in_first: int, +) -> None: + train_table, test_table = table.split(percentage_in_first) assert result_test_table == test_table + assert result_train_table.schema == train_table.schema assert result_train_table == train_table @pytest.mark.parametrize( "percentage_in_first", [ - 0.0, - 1.0, -1.0, 2.0, ], + ids=["-100%", "200%"], ) -def test_split_invalid(percentage_in_first: float) -> None: - table = Table.from_dict({"col1": [1, 2, 1], "col2": [1, 2, 4]}) +def test_should_raise_if_value_not_in_range(percentage_in_first: float) -> None: + table = Table({"col1": [1, 2, 1], "col2": [1, 2, 4]}) - with pytest.raises(ValueError, match="the given percentage is not in range"): + with pytest.raises(ValueError, match=r"The given percentage is not between 0 and 1"): table.split(percentage_in_first) + + +def test_should_split_empty_table() -> None: + t1, t2 = Table().split(0.4) + assert t1.number_of_rows == 0 + assert t2.number_of_rows == 0 diff --git a/tests/safeds/data/tabular/containers/_table/test_str.py b/tests/safeds/data/tabular/containers/_table/test_str.py new file mode 100644 index 000000000..34f5a05ab --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/test_str.py @@ -0,0 +1,19 @@ +import pytest +from safeds.data.tabular.containers import Table + + +@pytest.mark.parametrize( + ("table", "expected"), + [ + ( + Table({"col1": [1, 2, 1], "col2": [1, 2, 4]}), + " col1 col2\n0 1 1\n1 2 2\n2 1 4", + ), + (Table({"col1": [], "col2": []}), "Empty DataFrame\nColumns: [col1, col2]\nIndex: []"), + (Table(), "Empty DataFrame\nColumns: []\nIndex: []"), + (Table({"col1": [1], "col2": [1]}), " col1 col2\n0 1 1"), + ], + ids=["multiple rows", "rowless table", "empty table", "one row"], +) +def test_should_return_a_string_representation(table: Table, expected: str) -> None: + assert str(table) == expected diff --git a/tests/safeds/data/tabular/containers/_table/test_summary.py b/tests/safeds/data/tabular/containers/_table/test_summary.py index 3c414ab6b..f4abaa988 100644 --- a/tests/safeds/data/tabular/containers/_table/test_summary.py +++ b/tests/safeds/data/tabular/containers/_table/test_summary.py @@ -1,50 +1,140 @@ -from safeds.data.tabular.containers import Table +from statistics import stdev +import pytest +from safeds.data.tabular.containers import Table -def test_summary() -> None: - table = Table.from_dict({"col1": [1, 2, 1], "col2": ["a", "b", "c"]}) - truth = Table.from_dict( - { - "metrics": [ - "maximum", - "minimum", - "mean", - "mode", - "median", - "sum", - "variance", - "standard deviation", - "idness", - "stability", - "row count", - ], - "col1": [ - "2", - "1", - str(4.0 / 3), - "[1]", - "1.0", - "4", - str(1.0 / 3), - str(table._data["col1"].std()), - str(2.0 / 3), - str(2.0 / 3), - "3", - ], - "col2": [ - "-", - "-", - "-", - "['a', 'b', 'c']", - "-", - "-", - "-", - "-", - "1.0", - str(1.0 / 3), - "3", - ], - }, - ) - assert truth == table.summary() +@pytest.mark.parametrize( + ("table", "expected"), + [ + ( + Table({"col1": [1, 2, 1], "col2": ["a", "b", "c"]}), + Table( + { + "metrics": [ + "maximum", + "minimum", + "mean", + "mode", + "median", + "sum", + "variance", + "standard deviation", + "idness", + "stability", + ], + "col1": [ + "2", + "1", + str(4.0 / 3), + "[1]", + "1.0", + "4", + str(1.0 / 3), + str(stdev([1, 2, 1])), + str(2.0 / 3), + str(2.0 / 3), + ], + "col2": [ + "-", + "-", + "-", + "['a', 'b', 'c']", + "-", + "-", + "-", + "-", + "1.0", + str(1.0 / 3), + ], + }, + ), + ), + ( + Table(), + Table( + { + "metrics": [ + "maximum", + "minimum", + "mean", + "mode", + "median", + "sum", + "variance", + "standard deviation", + "idness", + "stability", + ], + }, + ), + ), + ( + Table({"col": [], "gg": []}), + Table( + { + "metrics": [ + "maximum", + "minimum", + "mean", + "mode", + "median", + "sum", + "variance", + "standard deviation", + "idness", + "stability", + ], + "col": [ + "-", + "-", + "-", + "-", + "-", + "-", + "-", + "-", + "-", + "-", + ], + "gg": [ + "-", + "-", + "-", + "-", + "-", + "-", + "-", + "-", + "-", + "-", + ], + }, + ), + ), + ( + Table({"col": [None, None]}), + Table( + { + "metrics": [ + "maximum", + "minimum", + "mean", + "mode", + "median", + "sum", + "variance", + "standard deviation", + "idness", + "stability", + ], + "col": ["-", "-", "-", "[]", "-", "-", "-", "-", "0.0", "-"], + }, + ), + ), + ], + ids=["Column of integers and Column of characters", "empty", "empty with columns", "Column of None"], +) +def test_should_make_summary(table: Table, expected: Table) -> None: + assert expected.schema == table.summary().schema + assert expected == table.summary() diff --git a/tests/safeds/data/tabular/containers/_table/test_table.py b/tests/safeds/data/tabular/containers/_table/test_table.py deleted file mode 100644 index 5ab205e4c..000000000 --- a/tests/safeds/data/tabular/containers/_table/test_table.py +++ /dev/null @@ -1,15 +0,0 @@ -from safeds.data.tabular.containers import Table -from safeds.data.tabular.typing import RealNumber, Schema - - -def test_create_empty_table() -> None: - table = Table([], Schema({"col1": RealNumber()})) - col = table.get_column("col1") - assert col.count() == 0 - assert isinstance(col.type, RealNumber) - assert col.name == "col1" - - -def test_create_empty_table_without_schema() -> None: - table = Table([]) - assert table.schema == Schema({}) diff --git a/tests/safeds/data/tabular/containers/_table/test_table_add_column.py b/tests/safeds/data/tabular/containers/_table/test_table_add_column.py deleted file mode 100644 index 4d2cf1287..000000000 --- a/tests/safeds/data/tabular/containers/_table/test_table_add_column.py +++ /dev/null @@ -1,29 +0,0 @@ -import pytest -from safeds.data.tabular.containers import Column, Table -from safeds.data.tabular.exceptions import ColumnSizeError, DuplicateColumnNameError - -from tests.helpers import resolve_resource_path - - -def test_table_add_column_valid() -> None: - input_table = Table.from_csv_file(resolve_resource_path("test_table_add_column_valid_input.csv")) - expected = Table.from_csv_file(resolve_resource_path("test_table_add_column_valid_output.csv")) - column = Column("C", ["a", "b", "c"]) - - result = input_table.add_column(column) - assert expected == result - - -@pytest.mark.parametrize( - ("column_values", "column_name", "error"), - [ - (["a", "b", "c"], "B", DuplicateColumnNameError), - (["a", "b"], "C", ColumnSizeError), - ], -) -def test_table_add_column_(column_values: list[str], column_name: str, error: type[Exception]) -> None: - input_table = Table.from_csv_file(resolve_resource_path("test_table_add_column_valid_input.csv")) - column = Column(column_name, column_values) - - with pytest.raises(error): - input_table.add_column(column) diff --git a/tests/safeds/data/tabular/containers/_table/test_to_columns.py b/tests/safeds/data/tabular/containers/_table/test_to_columns.py index f153b7961..b87957bbf 100644 --- a/tests/safeds/data/tabular/containers/_table/test_to_columns.py +++ b/tests/safeds/data/tabular/containers/_table/test_to_columns.py @@ -1,17 +1,11 @@ import pytest from safeds.data.tabular.containers import Column, Table -from tests.helpers import resolve_resource_path - @pytest.mark.parametrize( - ("values", "name", "index"), - [([1, 4], "A", 0), ([2, 5], "B", 1)], + ("table", "expected"), + [(Table({"A": [54, 74], "B": [90, 2010]}), [Column("A", [54, 74]), Column("B", [90, 2010])]), (Table(), [])], + ids=["normal", "empty"], ) -def test_to_columns(values: list[int], name: str, index: int) -> None: - table = Table.from_csv_file(resolve_resource_path("test_column_table.csv")) - columns_list: list[Column] = table.to_columns() - - column_expected: Column = Column(name, values) - - assert column_expected == columns_list[index] +def test_should_return_list_of_columns(table: Table, expected: list[Column]) -> None: + assert table.to_columns() == expected diff --git a/tests/safeds/data/tabular/containers/_table/test_to_csv_file.py b/tests/safeds/data/tabular/containers/_table/test_to_csv_file.py index 6d7359a94..86e1e76da 100644 --- a/tests/safeds/data/tabular/containers/_table/test_to_csv_file.py +++ b/tests/safeds/data/tabular/containers/_table/test_to_csv_file.py @@ -1,15 +1,60 @@ from pathlib import Path from tempfile import NamedTemporaryFile +import pytest from safeds.data.tabular.containers import Table +from safeds.exceptions import WrongFileExtensionError +from tests.helpers import resolve_resource_path -def test_to_csv_file() -> None: - table = Table.from_dict({"col1": ["col1_1"], "col2": ["col2_1"]}) - with NamedTemporaryFile() as tmp_table_file: + +@pytest.mark.parametrize( + "table", + [ + (Table({"col1": ["col1_1"], "col2": ["col2_1"]})), + (Table()), + ], + ids=["by String", "empty"], +) +def test_should_create_csv_file_from_table_by_str(table: Table) -> None: + with NamedTemporaryFile(suffix=".csv") as tmp_table_file: tmp_table_file.close() with Path(tmp_table_file.name).open("w", encoding="utf-8") as tmp_file: - table.to_csv_file(tmp_file.name) + table.to_csv_file(resolve_resource_path(tmp_file.name)) with Path(tmp_table_file.name).open("r", encoding="utf-8") as tmp_file: table_r = Table.from_csv_file(tmp_file.name) + assert table.schema == table_r.schema + assert table_r == table + + +@pytest.mark.parametrize( + "table", + [ + (Table({"col1": ["col1_1"], "col2": ["col2_1"]})), + (Table()), + ], + ids=["by String", "empty"], +) +def test_should_create_csv_file_from_table_by_path(table: Table) -> None: + with NamedTemporaryFile(suffix=".csv") as tmp_table_file: + tmp_table_file.close() + with Path(tmp_table_file.name).open("w", encoding="utf-8") as tmp_file: + table.to_csv_file(Path(tmp_file.name)) + with Path(tmp_table_file.name).open("r", encoding="utf-8") as tmp_file: + table_r = Table.from_csv_file(Path(tmp_file.name)) + assert table.schema == table_r.schema assert table == table_r + + +def test_should_raise_error_if_wrong_file_extension() -> None: + table = Table({"col1": ["col1_1"], "col2": ["col2_1"]}) + with NamedTemporaryFile(suffix=".invalid_file_extension") as tmp_table_file: + tmp_table_file.close() + with Path(tmp_table_file.name).open("w", encoding="utf-8") as tmp_file, pytest.raises( + WrongFileExtensionError, + match=( + r".invalid_file_extension has a wrong file extension. Please provide a file with the following" + r" extension\(s\): .csv" + ), + ): + table.to_csv_file(Path(tmp_file.name)) diff --git a/tests/safeds/data/tabular/containers/_table/test_to_dict.py b/tests/safeds/data/tabular/containers/_table/test_to_dict.py new file mode 100644 index 000000000..b6f7e16f0 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/test_to_dict.py @@ -0,0 +1,30 @@ +from typing import Any + +import pytest +from safeds.data.tabular.containers import Table + + +@pytest.mark.parametrize( + ("table", "expected"), + [ + ( + Table({}), + {}, + ), + ( + Table( + { + "a": [1], + "b": [2], + }, + ), + { + "a": [1], + "b": [2], + }, + ), + ], + ids=["Empty table", "Table with one row"], +) +def test_should_return_dict_for_table(table: Table, expected: dict[str, list[Any]]) -> None: + assert table.to_dict() == expected diff --git a/tests/safeds/data/tabular/containers/_table/test_to_excel_file.py b/tests/safeds/data/tabular/containers/_table/test_to_excel_file.py new file mode 100644 index 000000000..b2d200c0c --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/test_to_excel_file.py @@ -0,0 +1,58 @@ +from pathlib import Path +from tempfile import NamedTemporaryFile + +import pytest +from safeds.data.tabular.containers import Table +from safeds.exceptions import WrongFileExtensionError + + +@pytest.mark.parametrize( + "table", + [ + (Table({"col1": ["col1_1"], "col2": ["col2_1"]})), + (Table()), + ], + ids=["by String", "empty"], +) +def test_should_create_excel_file_from_table_by_str(table: Table) -> None: + with NamedTemporaryFile(suffix=".xlsx") as tmp_table_file: + tmp_table_file.close() + with Path(tmp_table_file.name).open("w", encoding="utf-8") as tmp_file: + table.to_excel_file(tmp_file.name) + with Path(tmp_table_file.name).open("r", encoding="utf-8") as tmp_file: + table_r = Table.from_excel_file(tmp_file.name) + assert table.schema == table_r.schema + assert table == table_r + + +@pytest.mark.parametrize( + "table", + [ + (Table({"col1": ["col1_1"], "col2": ["col2_1"]})), + (Table()), + ], + ids=["by String", "empty"], +) +def test_should_create_excel_file_from_table_by_path(table: Table) -> None: + with NamedTemporaryFile(suffix=".xlsx") as tmp_table_file: + tmp_table_file.close() + with Path(tmp_table_file.name).open("w", encoding="utf-8") as tmp_file: + table.to_excel_file(Path(tmp_file.name)) + with Path(tmp_table_file.name).open("r", encoding="utf-8") as tmp_file: + table_r = Table.from_excel_file(Path(tmp_file.name)) + assert table.schema == table_r.schema + assert table == table_r + + +def test_should_raise_error_if_wrong_file_extension() -> None: + table = Table({"col1": ["col1_1"], "col2": ["col2_1"]}) + with NamedTemporaryFile(suffix=".invalid_file_extension") as tmp_table_file: + tmp_table_file.close() + with Path(tmp_table_file.name).open("w", encoding="utf-8") as tmp_file, pytest.raises( + WrongFileExtensionError, + match=( + r".invalid_file_extension has a wrong file extension. Please provide a file with the following" + r" extension\(s\): \['.xls', '.xlsx', '.xlsm', '.xlsb', '.odf', '.ods', '.odt'\]" + ), + ): + table.to_excel_file(Path(tmp_file.name)) diff --git a/tests/safeds/data/tabular/containers/_table/test_to_html.py b/tests/safeds/data/tabular/containers/_table/test_to_html.py new file mode 100644 index 000000000..14c298593 --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/test_to_html.py @@ -0,0 +1,53 @@ +import re + +import pytest +from safeds.data.tabular.containers import Table + + +@pytest.mark.parametrize( + "table", + [ + Table({}), + Table({"a": [1, 2], "b": [3, 4]}), + ], + ids=[ + "empty", + "non-empty", + ], +) +def test_should_contain_table_element(table: Table) -> None: + pattern = r".*?" + assert re.search(pattern, table.to_html(), flags=re.S) is not None + + +@pytest.mark.parametrize( + "table", + [ + Table({}), + Table({"a": [1, 2], "b": [3, 4]}), + ], + ids=[ + "empty", + "non-empty", + ], +) +def test_should_contain_th_element_for_each_column_name(table: Table) -> None: + for column_name in table.column_names: + assert f"{column_name}" in table.to_html() + + +@pytest.mark.parametrize( + "table", + [ + Table({}), + Table({"a": [1, 2], "b": [3, 4]}), + ], + ids=[ + "empty", + "non-empty", + ], +) +def test_should_contain_td_element_for_each_value(table: Table) -> None: + for column in table.to_columns(): + for value in column: + assert f"{value}" in table.to_html() diff --git a/tests/safeds/data/tabular/containers/_table/test_to_json_file.py b/tests/safeds/data/tabular/containers/_table/test_to_json_file.py index db7a75271..c9e786bbe 100644 --- a/tests/safeds/data/tabular/containers/_table/test_to_json_file.py +++ b/tests/safeds/data/tabular/containers/_table/test_to_json_file.py @@ -1,15 +1,58 @@ from pathlib import Path from tempfile import NamedTemporaryFile +import pytest from safeds.data.tabular.containers import Table +from safeds.exceptions import WrongFileExtensionError -def test_to_json_file() -> None: - table = Table.from_dict({"col1": ["col1_1"], "col2": ["col2_1"]}) - with NamedTemporaryFile() as tmp_table_file: +@pytest.mark.parametrize( + "table", + [ + (Table({"col1": ["col1_1"], "col2": ["col2_1"]})), + (Table()), + ], + ids=["by String", "empty"], +) +def test_should_create_json_file_from_table_by_str(table: Table) -> None: + with NamedTemporaryFile(suffix=".json") as tmp_table_file: tmp_table_file.close() with Path(tmp_table_file.name).open("w", encoding="utf-8") as tmp_file: table.to_json_file(tmp_file.name) with Path(tmp_table_file.name).open("r", encoding="utf-8") as tmp_file: table_r = Table.from_json_file(tmp_file.name) + assert table.schema == table_r.schema assert table == table_r + + +@pytest.mark.parametrize( + "table", + [ + (Table({"col1": ["col1_1"], "col2": ["col2_1"]})), + (Table()), + ], + ids=["by String", "empty"], +) +def test_should_create_json_file_from_table_by_path(table: Table) -> None: + with NamedTemporaryFile(suffix=".json") as tmp_table_file: + tmp_table_file.close() + with Path(tmp_table_file.name).open("w", encoding="utf-8") as tmp_file: + table.to_json_file(Path(tmp_file.name)) + with Path(tmp_table_file.name).open("r", encoding="utf-8") as tmp_file: + table_r = Table.from_json_file(Path(tmp_file.name)) + assert table.schema == table_r.schema + assert table == table_r + + +def test_should_raise_error_if_wrong_file_extension() -> None: + table = Table({"col1": ["col1_1"], "col2": ["col2_1"]}) + with NamedTemporaryFile(suffix=".invalid_file_extension") as tmp_table_file: + tmp_table_file.close() + with Path(tmp_table_file.name).open("w", encoding="utf-8") as tmp_file, pytest.raises( + WrongFileExtensionError, + match=( + r".invalid_file_extension has a wrong file extension. Please provide a file with the following" + r" extension\(s\): .json" + ), + ): + table.to_json_file(Path(tmp_file.name)) diff --git a/tests/safeds/data/tabular/containers/_table/test_to_rows.py b/tests/safeds/data/tabular/containers/_table/test_to_rows.py index 35a7b89bf..92c695a7a 100644 --- a/tests/safeds/data/tabular/containers/_table/test_to_rows.py +++ b/tests/safeds/data/tabular/containers/_table/test_to_rows.py @@ -1,25 +1,39 @@ +import pandas as pd +import pytest from safeds.data.tabular.containers import Row, Table from safeds.data.tabular.typing import Integer, Schema, String -from tests.helpers import resolve_resource_path - -def test_to_rows() -> None: - table = Table.from_csv_file(resolve_resource_path("test_row_table.csv")) - expected_schema: Schema = Schema( - { - "A": Integer(), - "B": Integer(), - "D": String(), - }, - ) - rows_expected: list[Row] = [ - Row([1, 4, "d"], expected_schema), - Row([2, 5, "e"], expected_schema), - Row([3, 6, "f"], expected_schema), - ] - - rows_is: list[Row] = table.to_rows() +@pytest.mark.parametrize( + ("table", "rows_expected"), + [ + ( + Table._from_pandas_dataframe( + pd.DataFrame([[1, 4, "d"], [2, 5, "e"], [3, 6, "f"]]), + Schema({"A": Integer(), "B": Integer(), "D": String()}), + ), + [ + Row._from_pandas_dataframe( + pd.DataFrame({"A": [1], "B": [4], "D": ["d"]}), + Schema({"A": Integer(), "B": Integer(), "D": String()}), + ), + Row._from_pandas_dataframe( + pd.DataFrame({"A": [2], "B": [5], "D": ["e"]}), + Schema({"A": Integer(), "B": Integer(), "D": String()}), + ), + Row._from_pandas_dataframe( + pd.DataFrame({"A": [3], "B": [6], "D": ["f"]}), + Schema({"A": Integer(), "B": Integer(), "D": String()}), + ), + ], + ), + (Table(), []), + ], + ids=["normal", "empty"], +) +def test_should_return_list_of_rows(table: Table, rows_expected: list[Row]) -> None: + rows_is = table.to_rows() for row_is, row_expected in zip(rows_is, rows_expected, strict=True): + assert row_is.schema == row_expected.schema assert row_is == row_expected diff --git a/tests/safeds/data/tabular/containers/_table/test_transform_column.py b/tests/safeds/data/tabular/containers/_table/test_transform_column.py index 304ceaa5c..c3ee6f09c 100644 --- a/tests/safeds/data/tabular/containers/_table/test_transform_column.py +++ b/tests/safeds/data/tabular/containers/_table/test_transform_column.py @@ -1,20 +1,39 @@ import pytest from safeds.data.tabular.containers import Table -from safeds.data.tabular.exceptions import UnknownColumnNameError - -from tests.helpers import resolve_resource_path - - -def test_transform_column_valid() -> None: - input_table: Table = Table.from_csv_file(resolve_resource_path("test_table_transform_column.csv")) - - result: Table = input_table.transform_column("A", lambda row: row.get_value("A") * 2) - - assert result == Table.from_csv_file(resolve_resource_path("test_table_transform_column_output.csv")) - - -def test_transform_column_invalid() -> None: - input_table: Table = Table.from_csv_file(resolve_resource_path("test_table_transform_column.csv")) - - with pytest.raises(UnknownColumnNameError): - input_table.transform_column("D", lambda row: row.get_value("A") * 2) +from safeds.exceptions import UnknownColumnNameError + + +@pytest.mark.parametrize( + ("table", "table_transformed"), + [ + ( + Table({"A": [1, 2, 3], "B": [4, 5, 6], "C": ["a", "b", "c"]}), + Table({"A": [2, 4, 6], "B": [4, 5, 6], "C": ["a", "b", "c"]}), + ), + ], + ids=["multiply by 2"], +) +def test_should_transform_column(table: Table, table_transformed: Table) -> None: + result = table.transform_column("A", lambda row: row.get_value("A") * 2) + + assert result.schema == table_transformed.schema + assert result == table_transformed + + +@pytest.mark.parametrize( + "table", + [ + Table( + { + "A": [1, 2, 3], + "B": [4, 5, 6], + "C": ["a", "b", "c"], + }, + ), + Table(), + ], + ids=["column not found", "empty"], +) +def test_should_raise_if_column_not_found(table: Table) -> None: + with pytest.raises(UnknownColumnNameError, match=r"Could not find column\(s\) 'D'"): + table.transform_column("D", lambda row: row.get_value("A") * 2) diff --git a/tests/safeds/data/tabular/containers/_table/test_transform_table.py b/tests/safeds/data/tabular/containers/_table/test_transform_table.py new file mode 100644 index 000000000..eccae6b0f --- /dev/null +++ b/tests/safeds/data/tabular/containers/_table/test_transform_table.py @@ -0,0 +1,128 @@ +import pytest +from safeds.data.tabular.containers import Table +from safeds.data.tabular.transformation import OneHotEncoder +from safeds.exceptions import TransformerNotFittedError, UnknownColumnNameError + + +@pytest.mark.parametrize( + ("table", "column_names", "expected"), + [ + ( + Table( + { + "col1": ["a", "b", "b", "c"], + }, + ), + None, + Table( + { + "col1__a": [1.0, 0.0, 0.0, 0.0], + "col1__b": [0.0, 1.0, 1.0, 0.0], + "col1__c": [0.0, 0.0, 0.0, 1.0], + }, + ), + ), + ( + Table( + { + "col1": ["a", "b", "b", "c"], + "col2": ["a", "b", "b", "c"], + }, + ), + ["col1"], + Table( + { + "col1__a": [1.0, 0.0, 0.0, 0.0], + "col1__b": [0.0, 1.0, 1.0, 0.0], + "col1__c": [0.0, 0.0, 0.0, 1.0], + "col2": ["a", "b", "b", "c"], + }, + ), + ), + ( + Table( + { + "col1": ["a", "b", "b", "c"], + "col2": ["a", "b", "b", "c"], + }, + ), + ["col1", "col2"], + Table( + { + "col1__a": [1.0, 0.0, 0.0, 0.0], + "col1__b": [0.0, 1.0, 1.0, 0.0], + "col1__c": [0.0, 0.0, 0.0, 1.0], + "col2__a": [1.0, 0.0, 0.0, 0.0], + "col2__b": [0.0, 1.0, 1.0, 0.0], + "col2__c": [0.0, 0.0, 0.0, 1.0], + }, + ), + ), + ( + Table( + { + "col1": ["a", "b", "c"], + }, + ), + [], + Table( + { + "col1": ["a", "b", "c"], + }, + ), + ), + ], + ids=["all columns", "one column", "multiple columns", "none"], +) +def test_should_return_transformed_table( + table: Table, + column_names: list[str] | None, + expected: Table, +) -> None: + transformer = OneHotEncoder().fit(table, column_names) + assert table.transform_table(transformer).schema == expected.schema + assert table.transform_table(transformer) == expected + + +@pytest.mark.parametrize( + "table_to_fit", + [ + Table( + { + "col1": ["a", "b", "c"], + }, + ), + Table(), + ], + ids=["non-empty table", "empty table"], +) +def test_should_raise_if_column_not_found(table_to_fit: Table) -> None: + table_to_fit = Table( + { + "col1": ["a", "b", "c"], + }, + ) + + transformer = OneHotEncoder().fit(table_to_fit, None) + + table_to_transform = Table( + { + "col2": ["a", "b", "c"], + }, + ) + + with pytest.raises(UnknownColumnNameError, match=r"Could not find column\(s\) 'col1'"): + table_to_transform.transform_table(transformer) + + +def test_should_raise_if_not_fitted() -> None: + table = Table( + { + "col1": ["a", "b", "c"], + }, + ) + + transformer = OneHotEncoder() + + with pytest.raises(TransformerNotFittedError, match=r"The transformer has not been fitted yet."): + table.transform_table(transformer) diff --git a/tests/safeds/data/tabular/containers/test_row.py b/tests/safeds/data/tabular/containers/test_row.py index a0ce2abca..43fe0df93 100644 --- a/tests/safeds/data/tabular/containers/test_row.py +++ b/tests/safeds/data/tabular/containers/test_row.py @@ -1,56 +1,181 @@ +import re from typing import Any +import pandas as pd import pytest from safeds.data.tabular.containers import Row, Table -from safeds.data.tabular.exceptions import UnknownColumnNameError from safeds.data.tabular.typing import ColumnType, Integer, Schema, String +from safeds.exceptions import UnknownColumnNameError -class TestInit: +class TestFromDict: @pytest.mark.parametrize( - ("row", "expected"), + ("data", "expected"), + [ + ( + {}, + Row({}), + ), + ( + { + "a": 1, + "b": 2, + }, + Row({"a": 1, "b": 2}), + ), + ], + ids=[ + "empty", + "non-empty", + ], + ) + def test_should_create_row_from_dict(self, data: dict[str, Any], expected: Row) -> None: + assert Row.from_dict(data) == expected + + +class TestFromPandasDataFrame: + @pytest.mark.parametrize( + ("dataframe", "schema", "expected"), + [ + ( + pd.DataFrame({"col1": [0]}), + Schema({"col1": String()}), + Schema({"col1": String()}), + ), + ( + pd.DataFrame({"col1": [0], "col2": ["a"]}), + Schema({"col1": String(), "col2": String()}), + Schema({"col1": String(), "col2": String()}), + ), + ], + ids=[ + "one column", + "two columns", + ], + ) + def test_should_use_the_schema_if_passed(self, dataframe: pd.DataFrame, schema: Schema, expected: Schema) -> None: + row = Row._from_pandas_dataframe(dataframe, schema) + assert row._schema == expected + + @pytest.mark.parametrize( + ("dataframe", "expected"), [ - (Row([], Schema({})), Schema({})), - (Row([0], Schema({"col1": Integer()})), Schema({"col1": Integer()})), ( - Row([0, "a"], Schema({"col1": Integer(), "col2": String()})), + pd.DataFrame({"col1": [0]}), + Schema({"col1": Integer()}), + ), + ( + pd.DataFrame({"col1": [0], "col2": ["a"]}), Schema({"col1": Integer(), "col2": String()}), ), ], + ids=[ + "one column", + "two columns", + ], ) - def test_should_use_the_schema_if_passed(self, row: Row, expected: Schema) -> None: + def test_should_infer_the_schema_if_not_passed(self, dataframe: pd.DataFrame, expected: Schema) -> None: + row = Row._from_pandas_dataframe(dataframe) assert row._schema == expected + @pytest.mark.parametrize( + "dataframe", + [ + pd.DataFrame(), + pd.DataFrame({"col1": [0, 1]}), + ], + ids=[ + "empty", + "two rows", + ], + ) + def test_should_raise_if_dataframe_does_not_contain_exactly_one_row(self, dataframe: pd.DataFrame) -> None: + with pytest.raises(ValueError, match=re.escape("The dataframe has to contain exactly one row.")): + Row._from_pandas_dataframe(dataframe) + + +class TestInit: @pytest.mark.parametrize( ("row", "expected"), [ - (Row([]), Schema({})), - (Row([0]), Schema({"column_0": Integer()})), + (Row(), Schema({})), + (Row({}), Schema({})), + (Row({"col1": 0}), Schema({"col1": Integer()})), + ], + ids=[ + "empty", + "empty (explicit)", + "one column", ], ) - def test_should_infer_the_schema_if_not_passed(self, row: Row, expected: Schema) -> None: + def test_should_infer_the_schema(self, row: Row, expected: Schema) -> None: assert row._schema == expected +class TestContains: + @pytest.mark.parametrize( + ("row", "column_name", "expected"), + [ + (Row({}), "col1", False), + (Row({"col1": 0}), "col1", True), + (Row({"col1": 0}), "col2", False), + (Row({"col1": 0}), 1, False), + ], + ids=[ + "empty row", + "column exists", + "column does not exist", + "not a string", + ], + ) + def test_should_return_whether_the_row_has_the_column(self, row: Row, column_name: str, expected: bool) -> None: + assert (column_name in row) == expected + + class TestEq: @pytest.mark.parametrize( ("row1", "row2", "expected"), [ - (Row([]), Row([]), True), - (Row([0], Schema({"col1": Integer()})), Row([0], Schema({"col1": Integer()})), True), - (Row([0], Schema({"col1": Integer()})), Row([1], Schema({"col1": Integer()})), False), - (Row([0], Schema({"col1": Integer()})), Row([0], Schema({"col2": Integer()})), False), - (Row([0], Schema({"col1": Integer()})), Row(["a"], Schema({"col1": String()})), False), + (Row(), Row(), True), + (Row({"col1": 0}), Row({"col1": 0}), True), + (Row({"col1": 0}), Row({"col1": 1}), False), + (Row({"col1": 0}), Row({"col2": 0}), False), + (Row({"col1": 0}), Row({"col1": "a"}), False), + ], + ids=[ + "empty rows", + "equal rows", + "different values", + "different columns", + "different types", ], ) def test_should_return_whether_two_rows_are_equal(self, row1: Row, row2: Row, expected: bool) -> None: - assert (row1 == row2) == expected + assert (row1.__eq__(row2)) == expected + + @pytest.mark.parametrize( + "row", + [ + Row(), + Row({"col1": 0}), + ], + ids=[ + "empty", + "non-empty", + ], + ) + def test_should_return_true_if_objects_are_identical(self, row: Row) -> None: + assert (row.__eq__(row)) is True @pytest.mark.parametrize( ("row", "other"), [ - (Row([0], Schema({"col1": Integer()})), None), - (Row([0], Schema({"col1": Integer()})), Table([])), + (Row({"col1": 0}), None), + (Row({"col1": 0}), Table({})), + ], + ids=[ + "Row vs. None", + "Row vs. Table", ], ) def test_should_return_not_implemented_if_other_is_not_row(self, row: Row, other: Any) -> None: @@ -61,8 +186,12 @@ class TestGetitem: @pytest.mark.parametrize( ("row", "column_name", "expected"), [ - (Row([0], Schema({"col1": Integer()})), "col1", 0), - (Row([0, "a"], Schema({"col1": Integer(), "col2": String()})), "col2", "a"), + (Row({"col1": 0}), "col1", 0), + (Row({"col1": 0, "col2": "a"}), "col2", "a"), + ], + ids=[ + "one column", + "two columns", ], ) def test_should_return_the_value_in_the_column(self, row: Row, column_name: str, expected: Any) -> None: @@ -71,8 +200,12 @@ def test_should_return_the_value_in_the_column(self, row: Row, column_name: str, @pytest.mark.parametrize( ("row", "column_name"), [ - (Row([], Schema({})), "col1"), - (Row([0], Schema({"col1": Integer()})), "col2"), + (Row(), "col1"), + (Row({"col1": 0}), "col2"), + ], + ids=[ + "empty row", + "column does not exist", ], ) def test_should_raise_if_column_does_not_exist(self, row: Row, column_name: str) -> None: @@ -81,59 +214,116 @@ def test_should_raise_if_column_does_not_exist(self, row: Row, column_name: str) row[column_name] -class TestHash: +class TestIter: @pytest.mark.parametrize( - ("row1", "row2"), + ("row", "expected"), [ - (Row([]), Row([])), - (Row([0], Schema({"col1": Integer()})), Row([0], Schema({"col1": Integer()}))), + (Row(), []), + (Row({"col1": 0}), ["col1"]), + ], + ids=[ + "empty", + "non-empty", ], ) - def test_should_return_same_hash_for_equal_rows(self, row1: Row, row2: Row) -> None: - assert hash(row1) == hash(row2) + def test_should_return_an_iterator_for_the_column_names(self, row: Row, expected: list[str]) -> None: + assert list(row) == expected + +class TestLen: @pytest.mark.parametrize( - ("row1", "row2"), + ("row", "expected"), [ - (Row([0], Schema({"col1": Integer()})), Row([1], Schema({"col1": Integer()}))), - (Row([0], Schema({"col1": Integer()})), Row([0], Schema({"col2": Integer()}))), - (Row([0], Schema({"col1": Integer()})), Row(["a"], Schema({"col1": String()}))), + (Row(), 0), + (Row({"col1": 0, "col2": "a"}), 2), + ], + ids=[ + "empty", + "non-empty", ], ) - def test_should_return_different_hash_for_unequal_rows(self, row1: Row, row2: Row) -> None: - assert hash(row1) != hash(row2) + def test_should_return_the_number_of_columns(self, row: Row, expected: int) -> None: + assert len(row) == expected -class TestIter: +class TestRepr: @pytest.mark.parametrize( ("row", "expected"), [ - (Row([], Schema({})), []), - (Row([0], Schema({"col1": Integer()})), ["col1"]), + (Row(), "Row({})"), + (Row({"col1": 0}), "Row({'col1': 0})"), + (Row({"col1": 0, "col2": "a"}), "Row({\n 'col1': 0,\n 'col2': 'a'\n})"), + ], + ids=[ + "empty", + "single column", + "multiple columns", ], ) - def test_should_return_an_iterator_for_the_column_names(self, row: Row, expected: list[str]) -> None: - assert list(row) == expected + def test_should_return_a_string_representation(self, row: Row, expected: str) -> None: + assert repr(row) == expected -class TestLen: +class TestStr: @pytest.mark.parametrize( ("row", "expected"), [ - (Row([]), 0), - (Row([0, "a"]), 2), + (Row(), "{}"), + (Row({"col1": 0}), "{'col1': 0}"), + (Row({"col1": 0, "col2": "a"}), "{\n 'col1': 0,\n 'col2': 'a'\n}"), + ], + ids=[ + "empty", + "single column", + "multiple columns", + ], + ) + def test_should_return_a_string_representation(self, row: Row, expected: str) -> None: + assert str(row) == expected + + +class TestColumnNames: + @pytest.mark.parametrize( + ("row", "expected"), + [ + (Row(), []), + (Row({"col1": 0}), ["col1"]), + ], + ids=[ + "empty", + "non-empty", + ], + ) + def test_should_return_the_column_names(self, row: Row, expected: list[str]) -> None: + assert row.column_names == expected + + +class TestNumberOfColumns: + @pytest.mark.parametrize( + ("row", "expected"), + [ + (Row(), 0), + (Row({"col1": 0, "col2": "a"}), 2), + ], + ids=[ + "empty", + "non-empty", ], ) def test_should_return_the_number_of_columns(self, row: Row, expected: int) -> None: - assert len(row) == expected + assert row.number_of_column == expected class TestGetValue: @pytest.mark.parametrize( ("row", "column_name", "expected"), [ - (Row([0], Schema({"col1": Integer()})), "col1", 0), - (Row([0, "a"], Schema({"col1": Integer(), "col2": String()})), "col2", "a"), + (Row({"col1": 0}), "col1", 0), + (Row({"col1": 0, "col2": "a"}), "col2", "a"), + ], + ids=[ + "one column", + "two columns", ], ) def test_should_return_the_value_in_the_column(self, row: Row, column_name: str, expected: Any) -> None: @@ -142,8 +332,12 @@ def test_should_return_the_value_in_the_column(self, row: Row, column_name: str, @pytest.mark.parametrize( ("row", "column_name"), [ - (Row([], Schema({})), "col1"), - (Row([0], Schema({"col1": Integer()})), "col2"), + (Row({}), "col1"), + (Row({"col1": 0}), "col2"), + ], + ids=[ + "empty row", + "column does not exist", ], ) def test_should_raise_if_column_does_not_exist(self, row: Row, column_name: str) -> None: @@ -155,57 +349,177 @@ class TestHasColumn: @pytest.mark.parametrize( ("row", "column_name", "expected"), [ - (Row([], Schema({})), "col1", False), - (Row([0], Schema({"col1": Integer()})), "col1", True), - (Row([0], Schema({"col1": Integer()})), "col2", False), + (Row(), "col1", False), + (Row({"col1": 0}), "col1", True), + (Row({"col1": 0}), "col2", False), + ], + ids=[ + "empty row", + "column exists", + "column does not exist", ], ) def test_should_return_whether_the_row_has_the_column(self, row: Row, column_name: str, expected: bool) -> None: assert row.has_column(column_name) == expected -class TestGetColumnNames: +class TestGetColumnType: + @pytest.mark.parametrize( + ("row", "column_name", "expected"), + [ + (Row({"col1": 0}), "col1", Integer()), + (Row({"col1": 0, "col2": "a"}), "col2", String()), + ], + ids=[ + "one column", + "two columns", + ], + ) + def test_should_return_the_type_of_the_column(self, row: Row, column_name: str, expected: ColumnType) -> None: + assert row.get_column_type(column_name) == expected + + @pytest.mark.parametrize( + ("row", "column_name"), + [ + (Row(), "col1"), + (Row({"col1": 0}), "col2"), + ], + ids=[ + "empty row", + "column does not exist", + ], + ) + def test_should_raise_if_column_does_not_exist(self, row: Row, column_name: str) -> None: + with pytest.raises(UnknownColumnNameError): + row.get_column_type(column_name) + + +class TestToDict: @pytest.mark.parametrize( ("row", "expected"), [ - (Row([], Schema({})), []), - (Row([0], Schema({"col1": Integer()})), ["col1"]), + ( + Row(), + {}, + ), + ( + Row({"a": 1, "b": 2}), + { + "a": 1, + "b": 2, + }, + ), + ], + ids=[ + "empty", + "non-empty", + ], + ) + def test_should_return_dict_for_table(self, row: Row, expected: dict[str, Any]) -> None: + assert row.to_dict() == expected + + +class TestToHtml: + @pytest.mark.parametrize( + "row", + [ + Row(), + Row({"a": 1, "b": 2}), + ], + ids=[ + "empty", + "non-empty", ], ) - def test_should_return_the_column_names(self, row: Row, expected: list[str]) -> None: - assert row.get_column_names() == expected + def test_should_contain_table_element(self, row: Row) -> None: + pattern = r".*?" + assert re.search(pattern, row.to_html(), flags=re.S) is not None + @pytest.mark.parametrize( + "row", + [ + Row(), + Row({"a": 1, "b": 2}), + ], + ids=[ + "empty", + "non-empty", + ], + ) + def test_should_contain_th_element_for_each_column_name(self, row: Row) -> None: + for column_name in row.column_names: + assert f"{column_name}" in row.to_html() -class TestGetTypeOfColumn: @pytest.mark.parametrize( - ("row", "column_name", "expected"), + "row", [ - (Row([0], Schema({"col1": Integer()})), "col1", Integer()), - (Row([0, "a"], Schema({"col1": Integer(), "col2": String()})), "col2", String()), + Row(), + Row({"a": 1, "b": 2}), + ], + ids=[ + "empty", + "non-empty", ], ) - def test_should_return_the_type_of_the_column(self, row: Row, column_name: str, expected: ColumnType) -> None: - assert row.get_type_of_column(column_name) == expected + def test_should_contain_td_element_for_each_value(self, row: Row) -> None: + for value in row.values(): + assert f"{value}" in row.to_html() + +class TestReprHtml: @pytest.mark.parametrize( - ("row", "column_name"), + "row", [ - (Row([], Schema({})), "col1"), - (Row([0], Schema({"col1": Integer()})), "col2"), + Row(), + Row({"a": 1, "b": 2}), + ], + ids=[ + "empty", + "non-empty", ], ) - def test_should_raise_if_column_does_not_exist(self, row: Row, column_name: str) -> None: - with pytest.raises(UnknownColumnNameError): - row.get_type_of_column(column_name) + def test_should_contain_table_element(self, row: Row) -> None: + pattern = r".*?" + assert re.search(pattern, row._repr_html_(), flags=re.S) is not None + @pytest.mark.parametrize( + "row", + [ + Row(), + Row({"a": 1, "b": 2}), + ], + ids=[ + "empty", + "non-empty", + ], + ) + def test_should_contain_th_element_for_each_column_name(self, row: Row) -> None: + for column_name in row.column_names: + assert f"{column_name}" in row._repr_html_() -class TestCount: @pytest.mark.parametrize( - ("row", "expected"), + "row", [ - (Row([]), 0), - (Row([0, "a"]), 2), + Row(), + Row({"a": 1, "b": 2}), + ], + ids=[ + "empty", + "non-empty", ], ) - def test_should_return_the_number_of_columns(self, row: Row, expected: int) -> None: - assert row.count() == expected + def test_should_contain_td_element_for_each_value(self, row: Row) -> None: + for value in row.values(): + assert f"{value}" in row._repr_html_() + + +class TestCopy: + @pytest.mark.parametrize( + "row", + [Row(), Row({"a": [3, 0.1]})], + ids=["empty", "normal"], + ) + def test_should_copy_table(self, row: Row) -> None: + copied = row._copy() + assert copied == row + assert copied is not row diff --git a/tests/safeds/data/tabular/containers/test_table.py b/tests/safeds/data/tabular/containers/test_table.py deleted file mode 100644 index e99922056..000000000 --- a/tests/safeds/data/tabular/containers/test_table.py +++ /dev/null @@ -1,77 +0,0 @@ -from typing import Any - -import pytest -from pandas.core.interchange.from_dataframe import from_dataframe -from safeds.data.tabular.containers import Table -from safeds.data.tabular.exceptions import ColumnLengthMismatchError -from safeds.data.tabular.typing import Integer, Schema - - -class TestFromDict: - @pytest.mark.parametrize( - ("data", "expected"), - [ - ( - {}, - Table([]), - ), - ( - { - "a": [1], - "b": [2], - }, - Table([[1, 2]], schema=Schema({"a": Integer(), "b": Integer()})), - ), - ], - ) - def test_should_create_table_from_dict(self, data: dict[str, Any], expected: Table) -> None: - assert Table.from_dict(data) == expected - - def test_should_raise_if_columns_have_different_lengths(self) -> None: - with pytest.raises(ColumnLengthMismatchError): - Table.from_dict({"a": [1, 2], "b": [3]}) - - -class TestToDict: - @pytest.mark.parametrize( - ("table", "expected"), - [ - ( - Table([]), - {}, - ), - ( - Table([[1, 2]], schema=Schema({"a": Integer(), "b": Integer()})), - { - "a": [1], - "b": [2], - }, - ), - ], - ) - def test_should_return_dict_for_table(self, table: Table, expected: dict[str, Any]) -> None: - assert table.to_dict() == expected - - -class TestDataframe: - @pytest.mark.parametrize( - "table", - [ - Table([]), - Table.from_dict({"a": [1, 2], "b": [3, 4]}), - ], - ids=[ - "empty", - "non-empty", - ], - ) - def test_can_restore_table_from_exchange_object(self, table: Table) -> None: - exchange_object = table.__dataframe__() - restored = Table(from_dataframe(exchange_object)) - - assert restored == table - - def test_should_raise_if_allow_copy_is_false(self) -> None: - table = Table.from_dict({}) - with pytest.raises(NotImplementedError, match="`allow_copy` must be True"): - table.__dataframe__(allow_copy=False) diff --git a/tests/safeds/data/tabular/containers/test_tagged_table.py b/tests/safeds/data/tabular/containers/test_tagged_table.py deleted file mode 100644 index 3b6b9b445..000000000 --- a/tests/safeds/data/tabular/containers/test_tagged_table.py +++ /dev/null @@ -1,60 +0,0 @@ -import pytest -from safeds.data.tabular.containers import Column, Table, TaggedTable -from safeds.data.tabular.exceptions import UnknownColumnNameError - - -@pytest.fixture() -def table() -> Table: - return Table.from_dict( - { - "A": [1, 4], - "B": [2, 5], - "C": [3, 6], - "T": [0, 1], - }, - ) - - -@pytest.fixture() -def tagged_table(table: Table) -> TaggedTable: - return table.tag_columns(target_name="T") - - -class TestInit: - def test_should_raise_if_a_feature_does_not_exist(self, table: Table) -> None: - with pytest.raises(UnknownColumnNameError): - table.tag_columns(target_name="T", feature_names=["A", "B", "C", "D"]) - - def test_should_raise_if_target_does_not_exist(self, table: Table) -> None: - with pytest.raises(UnknownColumnNameError): - table.tag_columns(target_name="D") - - def test_should_raise_if_features_and_target_overlap(self, table: Table) -> None: - with pytest.raises(ValueError, match="Column 'A' cannot be both feature and target."): - table.tag_columns(target_name="A", feature_names=["A", "B", "C"]) - - def test_should_raise_if_features_are_empty_explicitly(self, table: Table) -> None: - with pytest.raises(ValueError, match="At least one feature column must be specified."): - table.tag_columns(target_name="A", feature_names=[]) - - def test_should_raise_if_features_are_empty_implicitly(self, table: Table) -> None: - table = Table.from_dict({"A": [1, 4]}) - - with pytest.raises(ValueError, match="At least one feature column must be specified."): - table.tag_columns(target_name="A") - - -class TestFeatures: - def test_should_return_features(self, tagged_table: TaggedTable) -> None: - assert tagged_table.features == Table.from_dict( - { - "A": [1, 4], - "B": [2, 5], - "C": [3, 6], - }, - ) - - -class TestTarget: - def test_should_return_target(self, tagged_table: TaggedTable) -> None: - assert tagged_table.target == Column("T", [0, 1]) diff --git a/tests/safeds/data/tabular/transformation/test_discretizer.py b/tests/safeds/data/tabular/transformation/test_discretizer.py new file mode 100644 index 000000000..b4a69971b --- /dev/null +++ b/tests/safeds/data/tabular/transformation/test_discretizer.py @@ -0,0 +1,301 @@ +import pytest +from safeds.data.tabular.containers import Table +from safeds.data.tabular.transformation import Discretizer +from safeds.exceptions import NonNumericColumnError, TransformerNotFittedError, UnknownColumnNameError + + +class TestInit: + def test_should_raise_value_error(self) -> None: + with pytest.raises(ValueError, match="Parameter 'number_of_bins' must be >= 2."): + _ = Discretizer(1) + + +class TestFit: + @pytest.mark.parametrize( + ("table", "columns", "error", "error_message"), + [ + ( + Table( + { + "col1": [0.0, 5.0, 5.0, 10.0], + }, + ), + ["col2"], + UnknownColumnNameError, + r"Could not find column\(s\) 'col2'", + ), + ( + Table( + { + "col1": [0.0, 5.0, 5.0, 10.0], + "col2": [0.0, 5.0, 5.0, 10.0], + "col3": [0.0, 5.0, 5.0, 10.0], + }, + ), + ["col4", "col5"], + UnknownColumnNameError, + r"Could not find column\(s\) 'col4, col5'", + ), + (Table(), ["col2"], ValueError, "The Discretizer cannot be fitted because the table contains 0 rows"), + ( + Table( + { + "col1": [0.0, 5.0, 5.0, 10.0], + "col2": ["a", "b", "c", "d"], + }, + ), + ["col2"], + NonNumericColumnError, + "Tried to do a numerical operation on one or multiple non-numerical columns: \ncol2 is of type String.", + ), + ], + ids=["UnknownColumnNameError", "multiple missing columns", "ValueError", "NonNumericColumnError"], + ) + def test_should_raise_errors( + self, + table: Table, + columns: list[str], + error: type[Exception], + error_message: str, + ) -> None: + with pytest.raises(error, match=error_message): + Discretizer().fit(table, columns) + + def test_should_not_change_original_transformer(self) -> None: + table = Table( + { + "col1": [0.0, 5.0, 10.0], + }, + ) + + transformer = Discretizer() + transformer.fit(table, None) + + assert transformer._wrapped_transformer is None + assert transformer._column_names is None + + +class TestTransform: + @pytest.mark.parametrize( + ("table_to_transform", "columns", "error", "error_message"), + [ + ( + Table( + { + "col2": ["a", "b", "c"], + }, + ), + ["col1"], + UnknownColumnNameError, + r"Could not find column\(s\) 'col1'", + ), + ( + Table( + { + "col2": ["a", "b", "c"], + }, + ), + ["col3", "col1"], + UnknownColumnNameError, + r"Could not find column\(s\) 'col3, col1'", + ), + (Table(), ["col1", "col3"], ValueError, "The table cannot be transformed because it contains 0 rows"), + ( + Table( + { + "col1": ["a", "b", "c", "d"], + }, + ), + ["col1"], + NonNumericColumnError, + "Tried to do a numerical operation on one or multiple non-numerical columns: \ncol1 is of type String.", + ), + ], + ids=["UnknownColumnNameError", "multiple missing columns", "ValueError", "NonNumericColumnError"], + ) + def test_should_raise_errors( + self, + table_to_transform: Table, + columns: list[str], + error: type[Exception], + error_message: str, + ) -> None: + table_to_fit = Table( + { + "col1": [0.0, 5.0, 10.0], + "col3": [0.0, 5.0, 10.0], + }, + ) + + transformer = Discretizer().fit(table_to_fit, columns) + + with pytest.raises(error, match=error_message): + transformer.transform(table_to_transform) + + def test_should_raise_if_not_fitted(self) -> None: + table = Table( + { + "col1": [0.0, 5.0, 10.0], + }, + ) + + transformer = Discretizer() + + with pytest.raises(TransformerNotFittedError, match=r"The transformer has not been fitted yet."): + transformer.transform(table) + + +class TestIsFitted: + def test_should_return_false_before_fitting(self) -> None: + transformer = Discretizer() + assert not transformer.is_fitted() + + def test_should_return_true_after_fitting(self) -> None: + table = Table( + { + "col1": [0.0, 5.0, 10.0], + }, + ) + + transformer = Discretizer() + fitted_transformer = transformer.fit(table, None) + assert fitted_transformer.is_fitted() + + +class TestFitAndTransform: + @pytest.mark.parametrize( + ("table", "column_names", "expected"), + [ + ( + Table( + { + "col1": [0.0, 5.0, 5.0, 10.0], + }, + ), + None, + Table( + { + "col1": [0.0, 2.0, 2.0, 3.0], + }, + ), + ), + ( + Table( + { + "col1": [0.0, 5.0, 5.0, 10.0], + "col2": [0.0, 5.0, 5.0, 10.0], + }, + ), + ["col1"], + Table( + { + "col1": [0.0, 2.0, 2.0, 3.0], + "col2": [0.0, 5.0, 5.0, 10.0], + }, + ), + ), + ], + ids=["None", "col1"], + ) + def test_should_return_transformed_table( + self, + table: Table, + column_names: list[str] | None, + expected: Table, + ) -> None: + assert Discretizer().fit_and_transform(table, column_names) == expected + + @pytest.mark.parametrize( + ("table", "number_of_bins", "expected"), + [ + ( + Table( + { + "col1": [0.0, 5.0, 5.0, 10.0], + }, + ), + 2, + Table( + { + "col1": [0, 1.0, 1.0, 1.0], + }, + ), + ), + ( + Table( + { + "col1": [0.0, 5.0, 5.0, 10.0], + }, + ), + 10, + Table( + { + "col1": [0.0, 4.0, 4.0, 7.0], + }, + ), + ), + ], + ids=["2", "10"], + ) + def test_should_return_transformed_table_with_correct_number_of_bins( + self, + table: Table, + number_of_bins: int, + expected: Table, + ) -> None: + assert Discretizer(number_of_bins).fit_and_transform(table, ["col1"]) == expected + + def test_should_not_change_original_table(self) -> None: + table = Table( + { + "col1": [0.0, 5.0, 10.0], + }, + ) + + Discretizer().fit_and_transform(table) + + expected = Table( + { + "col1": [0.0, 5.0, 10.0], + }, + ) + + assert table == expected + + def test_get_names_of_added_columns(self) -> None: + transformer = Discretizer() + with pytest.raises(TransformerNotFittedError, match=r"The transformer has not been fitted yet."): + transformer.get_names_of_added_columns() + + table = Table( + { + "a": [0.0], + }, + ) + transformer = transformer.fit(table, None) + assert transformer.get_names_of_added_columns() == [] + + def test_get_names_of_changed_columns(self) -> None: + transformer = Discretizer() + with pytest.raises(TransformerNotFittedError, match=r"The transformer has not been fitted yet."): + transformer.get_names_of_changed_columns() + table = Table( + { + "a": [0.0], + }, + ) + transformer = transformer.fit(table, None) + assert transformer.get_names_of_changed_columns() == ["a"] + + def test_get_names_of_removed_columns(self) -> None: + transformer = Discretizer() + with pytest.raises(TransformerNotFittedError, match=r"The transformer has not been fitted yet."): + transformer.get_names_of_removed_columns() + + table = Table( + { + "a": [0.0], + }, + ) + transformer = transformer.fit(table, None) + assert transformer.get_names_of_removed_columns() == [] diff --git a/tests/safeds/data/tabular/transformation/test_imputer.py b/tests/safeds/data/tabular/transformation/test_imputer.py index 8930e313b..b6353820f 100644 --- a/tests/safeds/data/tabular/transformation/test_imputer.py +++ b/tests/safeds/data/tabular/transformation/test_imputer.py @@ -1,81 +1,191 @@ +import warnings + import pytest from safeds.data.tabular.containers import Table -from safeds.data.tabular.exceptions import TransformerNotFittedError, UnknownColumnNameError from safeds.data.tabular.transformation import Imputer from safeds.data.tabular.typing import ImputerStrategy +from safeds.exceptions import NonNumericColumnError, TransformerNotFittedError, UnknownColumnNameError + + +def strategies() -> list[ImputerStrategy]: + """ + Return the list of imputer strategies to test. + + After you implemented a new imputer strategy, add it to this list to ensure + the tests run as expected with the new strategy. + + Returns + ------- + strategies : list[ImputerStrategy] + The list of classifiers to test. + """ + return [Imputer.Strategy.Constant(2), Imputer.Strategy.Mean(), Imputer.Strategy.Median(), Imputer.Strategy.Mode()] + + +class TestStrategy: + class TestStr: + @pytest.mark.parametrize( + ("strategy", "expected"), + [ + (Imputer.Strategy.Constant(0), "Constant(0)"), + (Imputer.Strategy.Mean(), "Mean"), + (Imputer.Strategy.Median(), "Median"), + (Imputer.Strategy.Mode(), "Mode"), + ], + ids=["Constant", "Mean", "Median", "Mode"], + ) + def test_should_return_correct_string_representation(self, strategy: ImputerStrategy, expected: str) -> None: + assert str(strategy) == expected class TestFit: - def test_should_raise_if_column_not_found(self) -> None: - table = Table.from_dict( + @pytest.mark.parametrize("strategy", strategies(), ids=lambda x: x.__class__.__name__) + def test_should_raise_if_column_not_found(self, strategy: ImputerStrategy) -> None: + table = Table( { "a": [1, 3, None], }, ) - with pytest.raises(UnknownColumnNameError): - Imputer(Imputer.Strategy.Constant(0)).fit(table, ["b"]) + with pytest.raises(UnknownColumnNameError, match=r"Could not find column\(s\) 'b, c'"): + Imputer(strategy).fit(table, ["b", "c"]) + + @pytest.mark.parametrize("strategy", strategies(), ids=lambda x: x.__class__.__name__) + def test_should_raise_if_table_contains_no_rows(self, strategy: ImputerStrategy) -> None: + with pytest.raises(ValueError, match=r"The Imputer cannot be fitted because the table contains 0 rows"): + Imputer(strategy).fit(Table({"col1": []}), ["col1"]) + + @pytest.mark.parametrize( + ("table", "col_names", "strategy"), + [ + (Table({"col1": [1, None, "ok"], "col2": [1, 2, "3"]}), ["col1", "col2"], Imputer.Strategy.Mean()), + (Table({"col1": [1, None, "ok"], "col2": [1, 2, "3"]}), ["col1", "col2"], Imputer.Strategy.Median()), + ], + ids=["Strategy Mean", "Strategy Median"], + ) + def test_should_raise_if_table_contains_non_numerical_data( + self, + table: Table, + col_names: list[str], + strategy: ImputerStrategy, + ) -> None: + with pytest.raises( + NonNumericColumnError, + match=r"Tried to do a numerical operation on one or multiple non-numerical columns: \n\['col1', 'col2'\]", + ): + Imputer(strategy).fit(table, col_names) + + @pytest.mark.parametrize( + ("table", "most_frequent"), + [ + (Table({"col1": [1, 2, 2, 1, 3]}), r"{'col1': \[1, 2\]}"), + (Table({"col1": ["a1", "a2", "a2", "a1", "a3"]}), r"{'col1': \['a1', 'a2'\]}"), + ( + Table({"col1": ["a1", "a2", "a2", "a1", "a3"], "col2": [1, 1, 2, 3, 3]}), + r"{'col1': \['a1', 'a2'\], 'col2': \[1, 3\]}", + ), + ], + ids=["integers", "strings", "multiple columns"], + ) + def test_should_warn_if_multiple_mode_values(self, table: Table, most_frequent: str) -> None: + with pytest.warns( + UserWarning, + match=( + r"There are multiple most frequent values in a column given to the Imputer.\nThe lowest values are" + r" being chosen in this cases. The following columns have multiple most frequent" + rf" values:\n{most_frequent}" + ), + ): + Imputer(Imputer.Strategy.Mode()).fit(table, None) - def test_should_not_change_original_transformer(self) -> None: - table = Table.from_dict( + @pytest.mark.parametrize("strategy", strategies(), ids=lambda x: x.__class__.__name__) + def test_should_not_change_original_transformer(self, strategy: ImputerStrategy) -> None: + table = Table( { - "a": [1, 3, None], + "a": [1, 3, 3, None], }, ) - transformer = Imputer(Imputer.Strategy.Constant(0)) - transformer.fit(table) + transformer = Imputer(strategy) + transformer.fit(table, None) assert transformer._wrapped_transformer is None assert transformer._column_names is None class TestTransform: - def test_should_raise_if_column_not_found(self) -> None: - table_to_fit = Table.from_dict( + @pytest.mark.parametrize("strategy", strategies(), ids=lambda x: x.__class__.__name__) + def test_should_raise_if_column_not_found(self, strategy: ImputerStrategy) -> None: + table_to_fit = Table( { - "a": [1, 3, None], + "a": [1, 3, 3, None], + "b": [1, 2, 3, 4], }, ) - transformer = Imputer(Imputer.Strategy.Constant(0)).fit(table_to_fit) + if isinstance(strategy, Imputer.Strategy.Mode): + with warnings.catch_warnings(): + warnings.filterwarnings( + action="ignore", + message=r"There are multiple most frequent values in a column given to the Imputer\..*", + category=UserWarning, + ) + transformer = Imputer(strategy).fit(table_to_fit, None) + else: + transformer = Imputer(strategy).fit(table_to_fit, None) - table_to_transform = Table.from_dict( + table_to_transform = Table( { - "b": [1, 3, None], + "c": [1, 3, 3, None], }, ) - with pytest.raises(UnknownColumnNameError): + with pytest.raises(UnknownColumnNameError, match=r"Could not find column\(s\) 'a, b'"): transformer.transform(table_to_transform) - def test_should_raise_if_not_fitted(self) -> None: - table = Table.from_dict( + @pytest.mark.parametrize("strategy", strategies(), ids=lambda x: x.__class__.__name__) + def test_should_raise_if_table_contains_no_rows(self, strategy: ImputerStrategy) -> None: + with pytest.raises(ValueError, match=r"The Imputer cannot transform the table because it contains 0 rows"): + Imputer(strategy).fit(Table({"col1": [1, 2, 2]}), ["col1"]).transform(Table({"col1": []})) + + @pytest.mark.parametrize("strategy", strategies(), ids=lambda x: x.__class__.__name__) + def test_should_raise_if_not_fitted(self, strategy: ImputerStrategy) -> None: + table = Table( { "a": [1, 3, None], }, ) - transformer = Imputer(Imputer.Strategy.Constant(0)) + transformer = Imputer(strategy) - with pytest.raises(TransformerNotFittedError): + with pytest.raises(TransformerNotFittedError, match=r"The transformer has not been fitted yet."): transformer.transform(table) + @pytest.mark.parametrize("strategy", strategies(), ids=lambda x: x.__class__.__name__) + def test_should_warn_if_no_missing_values(self, strategy: ImputerStrategy) -> None: + with pytest.warns( + UserWarning, + match=r"The columns \['col1'\] have no missing values, so the Imputer did not change these columns", + ): + Imputer(strategy).fit(Table({"col1": [1, 2, 3]}), ["col1"]).transform(Table({"col1": [1, 2, 3, 4, 5]})) + class TestIsFitted: - def test_should_return_false_before_fitting(self) -> None: - transformer = Imputer(Imputer.Strategy.Mean()) + @pytest.mark.parametrize("strategy", strategies(), ids=lambda x: x.__class__.__name__) + def test_should_return_false_before_fitting(self, strategy: ImputerStrategy) -> None: + transformer = Imputer(strategy) assert not transformer.is_fitted() - def test_should_return_true_after_fitting(self) -> None: - table = Table.from_dict( + @pytest.mark.parametrize("strategy", strategies(), ids=lambda x: x.__class__.__name__) + def test_should_return_true_after_fitting(self, strategy: ImputerStrategy) -> None: + table = Table( { - "a": [1, 3, None], + "a": [1, 3, 3, None], }, ) - transformer = Imputer(Imputer.Strategy.Mean()) - fitted_transformer = transformer.fit(table) + transformer = Imputer(strategy) + fitted_transformer = transformer.fit(table, None) assert fitted_transformer.is_fitted() @@ -84,63 +194,63 @@ class TestFitAndTransform: ("table", "column_names", "strategy", "expected"), [ ( - Table.from_dict( + Table( { "a": [1.0, 3.0, None], }, ), None, Imputer.Strategy.Constant(0.0), - Table.from_dict( + Table( { "a": [1.0, 3.0, 0.0], }, ), ), ( - Table.from_dict( + Table( { "a": [1.0, 3.0, None], }, ), None, Imputer.Strategy.Mean(), - Table.from_dict( + Table( { "a": [1.0, 3.0, 2.0], }, ), ), ( - Table.from_dict( + Table( { "a": [1.0, 3.0, 1.0, None], }, ), None, Imputer.Strategy.Median(), - Table.from_dict( + Table( { "a": [1.0, 3.0, 1.0, 1.0], }, ), ), ( - Table.from_dict( + Table( { "a": [1.0, 3.0, 3.0, None], }, ), None, Imputer.Strategy.Mode(), - Table.from_dict( + Table( { "a": [1.0, 3.0, 3.0, 3.0], }, ), ), ( - Table.from_dict( + Table( { "a": [1.0, 3.0, None], "b": [1.0, 3.0, None], @@ -148,13 +258,31 @@ class TestFitAndTransform: ), ["a"], Imputer.Strategy.Constant(0.0), - Table.from_dict( + Table( { "a": [1.0, 3.0, 0.0], "b": [1.0, 3.0, None], }, ), ), + ( + Table( + { + "a": [1.0, 1.0, 2.0, 2.0, None], + }, + ), + ["a"], + Imputer.Strategy.Mode(), + Table({"a": [1.0, 1.0, 2.0, 2.0, 1.0]}), + ), + ], + ids=[ + "constant strategy", + "mean strategy", + "median strategy", + "mode strategy", + "constant strategy multiple columns", + "mode strategy multiple most frequent values", ], ) def test_should_return_transformed_table( @@ -164,31 +292,75 @@ def test_should_return_transformed_table( strategy: ImputerStrategy, expected: Table, ) -> None: - assert Imputer(strategy).fit_and_transform(table, column_names) == expected + if isinstance(strategy, Imputer.Strategy.Mode): + with warnings.catch_warnings(): + warnings.filterwarnings( + action="ignore", + message=r"There are multiple most frequent values in a column given to the Imputer\..*", + category=UserWarning, + ) + assert Imputer(strategy).fit_and_transform(table, column_names) == expected + else: + assert Imputer(strategy).fit_and_transform(table, column_names) == expected - def test_should_raise_if_strategy_is_mode_but_multiple_values_are_most_frequent(self) -> None: - table = Table.from_dict( + @pytest.mark.parametrize("strategy", strategies(), ids=lambda x: x.__class__.__name__) + def test_should_not_change_original_table(self, strategy: ImputerStrategy) -> None: + table = Table( { - "a": [1, 2, 3, None], + "a": [1, None, None], }, ) - with pytest.raises(IndexError): - Imputer(Imputer.Strategy.Mode()).fit_and_transform(table) + Imputer(strategy=strategy).fit_and_transform(table) - def test_should_not_change_original_table(self) -> None: - table = Table.from_dict( + expected = Table( { "a": [1, None, None], }, ) - Imputer(strategy=Imputer.Strategy.Constant(1)).fit_and_transform(table) + assert table == expected + + @pytest.mark.parametrize("strategy", strategies(), ids=lambda x: x.__class__.__name__) + def test_get_names_of_added_columns(self, strategy: ImputerStrategy) -> None: + transformer = Imputer(strategy=strategy) + with pytest.raises(TransformerNotFittedError): + transformer.get_names_of_added_columns() - expected = Table.from_dict( + table = Table( { - "a": [1, None, None], + "a": [1, None], + "b": [1, 1], }, ) + transformer = transformer.fit(table, None) + assert transformer.get_names_of_added_columns() == [] - assert table == expected + @pytest.mark.parametrize("strategy", strategies(), ids=lambda x: x.__class__.__name__) + def test_get_names_of_changed_columns(self, strategy: ImputerStrategy) -> None: + transformer = Imputer(strategy=strategy) + with pytest.raises(TransformerNotFittedError): + transformer.get_names_of_changed_columns() + table = Table( + { + "a": [1, None], + "b": [1, 1], + }, + ) + transformer = transformer.fit(table, None) + assert transformer.get_names_of_changed_columns() == ["a", "b"] + + @pytest.mark.parametrize("strategy", strategies(), ids=lambda x: x.__class__.__name__) + def test_get_names_of_removed_columns(self, strategy: ImputerStrategy) -> None: + transformer = Imputer(strategy=strategy) + with pytest.raises(TransformerNotFittedError): + transformer.get_names_of_removed_columns() + + table = Table( + { + "a": [1, None], + "b": [1, 1], + }, + ) + transformer = transformer.fit(table, None) + assert transformer.get_names_of_removed_columns() == [] diff --git a/tests/safeds/data/tabular/transformation/test_label_encoder.py b/tests/safeds/data/tabular/transformation/test_label_encoder.py index a731d7793..57614335d 100644 --- a/tests/safeds/data/tabular/transformation/test_label_encoder.py +++ b/tests/safeds/data/tabular/transformation/test_label_encoder.py @@ -1,29 +1,43 @@ import pytest from safeds.data.tabular.containers import Table -from safeds.data.tabular.exceptions import TransformerNotFittedError, UnknownColumnNameError from safeds.data.tabular.transformation import LabelEncoder +from safeds.exceptions import NonNumericColumnError, TransformerNotFittedError, UnknownColumnNameError class TestFit: def test_should_raise_if_column_not_found(self) -> None: - table = Table.from_dict( + table = Table( { "col1": ["a", "b", "c"], }, ) - with pytest.raises(UnknownColumnNameError): - LabelEncoder().fit(table, ["col2"]) + with pytest.raises(UnknownColumnNameError, match=r"Could not find column\(s\) 'col2, col3'"): + LabelEncoder().fit(table, ["col2", "col3"]) + + def test_should_raise_if_table_contains_no_rows(self) -> None: + with pytest.raises(ValueError, match=r"The LabelEncoder cannot transform the table because it contains 0 rows"): + LabelEncoder().fit(Table({"col1": []}), ["col1"]) + + def test_should_warn_if_table_contains_numerical_data(self) -> None: + with pytest.warns( + UserWarning, + match=( + r"The columns \['col1'\] contain numerical data. The LabelEncoder is designed to encode non-numerical" + r" values into numerical values" + ), + ): + LabelEncoder().fit(Table({"col1": [1, 2]}), ["col1"]) def test_should_not_change_original_transformer(self) -> None: - table = Table.from_dict( + table = Table( { "col1": ["a", "b", "c"], }, ) transformer = LabelEncoder() - transformer.fit(table) + transformer.fit(table, None) assert transformer._wrapped_transformer is None assert transformer._column_names is None @@ -31,25 +45,26 @@ def test_should_not_change_original_transformer(self) -> None: class TestTransform: def test_should_raise_if_column_not_found(self) -> None: - table_to_fit = Table.from_dict( + table_to_fit = Table( { "col1": ["a", "b", "c"], + "col2": ["d", "e", "f"], }, ) - transformer = LabelEncoder().fit(table_to_fit) + transformer = LabelEncoder().fit(table_to_fit, None) - table_to_transform = Table.from_dict( + table_to_transform = Table( { - "col2": ["a", "b", "c"], + "col3": ["a", "b", "c"], }, ) - with pytest.raises(UnknownColumnNameError): + with pytest.raises(UnknownColumnNameError, match=r"Could not find column\(s\) 'col1, col2'"): transformer.transform(table_to_transform) def test_should_raise_if_not_fitted(self) -> None: - table = Table.from_dict( + table = Table( { "col1": ["a", "b", "c"], }, @@ -57,9 +72,13 @@ def test_should_raise_if_not_fitted(self) -> None: transformer = LabelEncoder() - with pytest.raises(TransformerNotFittedError): + with pytest.raises(TransformerNotFittedError, match=r"The transformer has not been fitted yet."): transformer.transform(table) + def test_should_raise_if_table_contains_no_rows(self) -> None: + with pytest.raises(ValueError, match=r"The LabelEncoder cannot transform the table because it contains 0 rows"): + LabelEncoder().fit(Table({"col1": ["one", "two"]}), ["col1"]).transform(Table({"col1": []})) + class TestIsFitted: def test_should_return_false_before_fitting(self) -> None: @@ -67,14 +86,14 @@ def test_should_return_false_before_fitting(self) -> None: assert not transformer.is_fitted() def test_should_return_true_after_fitting(self) -> None: - table = Table.from_dict( + table = Table( { "col1": ["a", "b", "c"], }, ) transformer = LabelEncoder() - fitted_transformer = transformer.fit(table) + fitted_transformer = transformer.fit(table, None) assert fitted_transformer.is_fitted() @@ -83,27 +102,27 @@ class TestFitAndTransform: ("table", "column_names", "expected"), [ ( - Table.from_dict( + Table( { "col1": ["a", "b", "b", "c"], }, ), None, - Table.from_dict( + Table( { "col1": [0.0, 1.0, 1.0, 2.0], }, ), ), ( - Table.from_dict( + Table( { "col1": ["a", "b", "b", "c"], "col2": ["a", "b", "b", "c"], }, ), ["col1"], - Table.from_dict( + Table( { "col1": [0.0, 1.0, 1.0, 2.0], "col2": ["a", "b", "b", "c"], @@ -111,6 +130,7 @@ class TestFitAndTransform: ), ), ], + ids=["no_column_names", "with_column_names"], ) def test_should_return_transformed_table( self, @@ -121,7 +141,7 @@ def test_should_return_transformed_table( assert LabelEncoder().fit_and_transform(table, column_names) == expected def test_should_not_change_original_table(self) -> None: - table = Table.from_dict( + table = Table( { "col1": ["a", "b", "c"], }, @@ -129,7 +149,7 @@ def test_should_not_change_original_table(self) -> None: LabelEncoder().fit_and_transform(table) - expected = Table.from_dict( + expected = Table( { "col1": ["a", "b", "c"], }, @@ -137,35 +157,74 @@ def test_should_not_change_original_table(self) -> None: assert table == expected + def test_get_names_of_added_columns(self) -> None: + transformer = LabelEncoder() + with pytest.raises(TransformerNotFittedError): + transformer.get_names_of_added_columns() + + table = Table( + { + "a": ["b"], + }, + ) + transformer = transformer.fit(table, None) + assert transformer.get_names_of_added_columns() == [] + + def test_get_names_of_changed_columns(self) -> None: + transformer = LabelEncoder() + with pytest.raises(TransformerNotFittedError): + transformer.get_names_of_changed_columns() + table = Table( + { + "a": ["b"], + }, + ) + transformer = transformer.fit(table, None) + assert transformer.get_names_of_changed_columns() == ["a"] + + def test_get_names_of_removed_columns(self) -> None: + transformer = LabelEncoder() + with pytest.raises(TransformerNotFittedError): + transformer.get_names_of_removed_columns() + + table = Table( + { + "a": ["b"], + }, + ) + transformer = transformer.fit(table, None) + assert transformer.get_names_of_removed_columns() == [] + class TestInverseTransform: @pytest.mark.parametrize( "table", [ - Table.from_dict( + Table( { "col1": ["a", "b", "b", "c"], }, ), ], + ids=["no_column_names"], ) def test_should_return_original_table(self, table: Table) -> None: - transformer = LabelEncoder().fit(table) + transformer = LabelEncoder().fit(table, None) assert transformer.inverse_transform(transformer.transform(table)) == table def test_should_not_change_transformed_table(self) -> None: - table = Table.from_dict( + table = Table( { "col1": ["a", "b", "c"], }, ) - transformer = LabelEncoder().fit(table) + transformer = LabelEncoder().fit(table, None) transformed_table = transformer.transform(table) transformer.inverse_transform(transformed_table) - expected = Table.from_dict( + expected = Table( { "col1": [0.0, 1.0, 2.0], }, @@ -174,7 +233,7 @@ def test_should_not_change_transformed_table(self) -> None: assert transformed_table == expected def test_should_raise_if_not_fitted(self) -> None: - table = Table.from_dict( + table = Table( { "col1": [0.0, 1.0, 1.0, 2.0], }, @@ -182,5 +241,29 @@ def test_should_raise_if_not_fitted(self) -> None: transformer = LabelEncoder() - with pytest.raises(TransformerNotFittedError): + with pytest.raises(TransformerNotFittedError, match=r"The transformer has not been fitted yet."): transformer.inverse_transform(table) + + def test_should_raise_if_column_not_found(self) -> None: + with pytest.raises(UnknownColumnNameError, match=r"Could not find column\(s\) 'col1, col2'"): + LabelEncoder().fit( + Table({"col1": ["one", "two"], "col2": ["three", "four"]}), + ["col1", "col2"], + ).inverse_transform(Table({"col3": [1.0, 0.0]})) + + def test_should_raise_if_table_contains_non_numerical_data(self) -> None: + with pytest.raises( + NonNumericColumnError, + match=r"Tried to do a numerical operation on one or multiple non-numerical columns: \n\['col1', 'col2'\]", + ): + LabelEncoder().fit( + Table({"col1": ["one", "two"], "col2": ["three", "four"]}), + ["col1", "col2"], + ).inverse_transform(Table({"col1": ["1", "null"], "col2": ["2", "apple"]})) + + def test_should_raise_if_table_contains_no_rows(self) -> None: + with pytest.raises( + ValueError, + match=r"The LabelEncoder cannot inverse transform the table because it contains 0 rows", + ): + LabelEncoder().fit(Table({"col1": ["one", "two"]}), ["col1"]).inverse_transform(Table({"col1": []})) diff --git a/tests/safeds/data/tabular/transformation/test_one_hot_encoder.py b/tests/safeds/data/tabular/transformation/test_one_hot_encoder.py index a57f3e739..810f9bd24 100644 --- a/tests/safeds/data/tabular/transformation/test_one_hot_encoder.py +++ b/tests/safeds/data/tabular/transformation/test_one_hot_encoder.py @@ -1,55 +1,88 @@ +import warnings + import pytest from safeds.data.tabular.containers import Table -from safeds.data.tabular.exceptions import TransformerNotFittedError, UnknownColumnNameError from safeds.data.tabular.transformation import OneHotEncoder +from safeds.exceptions import ( + NonNumericColumnError, + TransformerNotFittedError, + UnknownColumnNameError, + ValueNotPresentWhenFittedError, +) class TestFit: def test_should_raise_if_column_not_found(self) -> None: - table = Table.from_dict( + table = Table( { "col1": ["a", "b", "c"], }, ) - with pytest.raises(UnknownColumnNameError): - OneHotEncoder().fit(table, ["col2"]) + with pytest.raises(UnknownColumnNameError, match=r"Could not find column\(s\) 'col2, col3'"): + OneHotEncoder().fit(table, ["col2", "col3"]) - def test_should_not_change_original_transformer(self) -> None: - table = Table.from_dict( - { - "col1": ["a", "b", "c"], - }, - ) + def test_should_raise_if_table_contains_no_rows(self) -> None: + with pytest.raises(ValueError, match=r"The OneHotEncoder cannot be fitted because the table contains 0 rows"): + OneHotEncoder().fit(Table({"col1": []}), ["col1"]) + def test_should_warn_if_table_contains_numerical_data(self) -> None: + with pytest.warns( + UserWarning, + match=( + r"The columns \['col1'\] contain numerical data. The OneHotEncoder is designed to encode non-numerical" + r" values into numerical values" + ), + ): + OneHotEncoder().fit(Table({"col1": [1, 2, 3]}), ["col1"]) + + @pytest.mark.parametrize( + "table", + [ + Table( + { + "col1": ["a", "b", "c"], + }, + ), + Table( + { + "col1": ["a", "b", float("nan")], + }, + ), + ], + ids=["string table", "table with nan"], + ) + def test_should_not_change_original_transformer(self, table: Table) -> None: transformer = OneHotEncoder() - transformer.fit(table) + transformer.fit(table, None) - assert transformer._wrapped_transformer is None assert transformer._column_names is None + assert transformer._value_to_column is None + assert transformer._value_to_column_nans is None class TestTransform: def test_should_raise_if_column_not_found(self) -> None: - table_to_fit = Table.from_dict( + table_to_fit = Table( { "col1": ["a", "b", "c"], + "col2": ["a2", "b2", "c2"], }, ) - transformer = OneHotEncoder().fit(table_to_fit) + transformer = OneHotEncoder().fit(table_to_fit, None) - table_to_transform = Table.from_dict( + table_to_transform = Table( { - "col2": ["a", "b", "c"], + "col3": ["a", "b", "c"], }, ) - with pytest.raises(UnknownColumnNameError): + with pytest.raises(UnknownColumnNameError, match=r"Could not find column\(s\) 'col1, col2'"): transformer.transform(table_to_transform) def test_should_raise_if_not_fitted(self) -> None: - table = Table.from_dict( + table = Table( { "col1": ["a", "b", "c"], }, @@ -57,24 +90,66 @@ def test_should_raise_if_not_fitted(self) -> None: transformer = OneHotEncoder() - with pytest.raises(TransformerNotFittedError): + with pytest.raises(TransformerNotFittedError, match=r"The transformer has not been fitted yet."): transformer.transform(table) + def test_should_raise_if_table_contains_no_rows(self) -> None: + with pytest.raises(ValueError, match=r"The LabelEncoder cannot transform the table because it contains 0 rows"): + OneHotEncoder().fit(Table({"col1": ["one", "two", "three"]}), ["col1"]).transform(Table({"col1": []})) + + def test_should_raise_value_not_present_when_fitted(self) -> None: + fit_table = Table( + {"col1": ["a"], "col2": ["b"]}, + ) + transform_table = Table( + {"col1": ["b", "c"], "col2": ["a", "b"]}, + ) + + transformer = OneHotEncoder().fit(fit_table, None) + + with pytest.raises( + ValueNotPresentWhenFittedError, + match=( + r"Value\(s\) not present in the table the transformer was fitted on: \nb in column col1\nc in column" + r" col1\na in column col2" + ), + ): + transformer.transform(transform_table) + class TestIsFitted: def test_should_return_false_before_fitting(self) -> None: transformer = OneHotEncoder() assert not transformer.is_fitted() - def test_should_return_true_after_fitting(self) -> None: - table = Table.from_dict( - { - "col1": ["a", "b", "c"], - }, - ) - + @pytest.mark.parametrize( + "table", + [ + Table( + { + "col1": ["a", "b", "c"], + }, + ), + Table( + { + "col1": [float("nan")], + }, + ), + ], + ids=["table with strings", "table with nans"], + ) + def test_should_return_true_after_fitting(self, table: Table) -> None: transformer = OneHotEncoder() - fitted_transformer = transformer.fit(table) + with warnings.catch_warnings(): + warnings.filterwarnings( + action="ignore", + message=( + r"The columns .+ contain numerical data. The OneHotEncoder is designed to encode non-numerical " + r"values into numerical values" + ), + category=UserWarning, + ) + fitted_transformer = transformer.fit(table, None) assert fitted_transformer.is_fitted() @@ -83,58 +158,110 @@ class TestFitAndTransform: ("table", "column_names", "expected"), [ ( - Table.from_dict( + Table( { "col1": ["a", "b", "b", "c"], }, ), None, - Table.from_dict( + Table( { - "col1_a": [1.0, 0.0, 0.0, 0.0], - "col1_b": [0.0, 1.0, 1.0, 0.0], - "col1_c": [0.0, 0.0, 0.0, 1.0], + "col1__a": [1.0, 0.0, 0.0, 0.0], + "col1__b": [0.0, 1.0, 1.0, 0.0], + "col1__c": [0.0, 0.0, 0.0, 1.0], }, ), ), ( - Table.from_dict( + Table( { "col1": ["a", "b", "b", "c"], "col2": ["a", "b", "b", "c"], }, ), ["col1"], - Table.from_dict( + Table( { - "col1_a": [1.0, 0.0, 0.0, 0.0], - "col1_b": [0.0, 1.0, 1.0, 0.0], - "col1_c": [0.0, 0.0, 0.0, 1.0], + "col1__a": [1.0, 0.0, 0.0, 0.0], + "col1__b": [0.0, 1.0, 1.0, 0.0], + "col1__c": [0.0, 0.0, 0.0, 1.0], "col2": ["a", "b", "b", "c"], }, ), ), ( - Table.from_dict( + Table( { "col1": ["a", "b", "b", "c"], "col2": ["a", "b", "b", "c"], }, ), ["col1", "col2"], - Table.from_dict( + Table( + { + "col1__a": [1.0, 0.0, 0.0, 0.0], + "col1__b": [0.0, 1.0, 1.0, 0.0], + "col1__c": [0.0, 0.0, 0.0, 1.0], + "col2__a": [1.0, 0.0, 0.0, 0.0], + "col2__b": [0.0, 1.0, 1.0, 0.0], + "col2__c": [0.0, 0.0, 0.0, 1.0], + }, + ), + ), + ( + Table( + { + "a_b": ["c"], + "a": ["b_c"], + }, + ), + None, + Table( { - "col1_a": [1.0, 0.0, 0.0, 0.0], - "col1_b": [0.0, 1.0, 1.0, 0.0], - "col1_c": [0.0, 0.0, 0.0, 1.0], - "col2_a": [1.0, 0.0, 0.0, 0.0], - "col2_b": [0.0, 1.0, 1.0, 0.0], - "col2_c": [0.0, 0.0, 0.0, 1.0], + "a_b__c": [1.0], + "a__b_c": [1.0], }, ), ), + ( + Table( + { + "a__b": ["c", "d"], + "a": ["b__c", "d"], + }, + ), + None, + Table( + { + "a__b__c": [1.0, 0.0], + "a__b__d": [0.0, 1.0], + "a__b__c#2": [1.0, 0.0], + "a__d": [0.0, 1.0], + }, + ), + ), + ( + Table({"a": ["a", "b", "c", "c"], "b": ["a", float("nan"), float("nan"), "a"]}), + None, + Table( + { + "a__a": [1.0, 0.0, 0.0, 0.0], + "a__b": [0.0, 1.0, 0.0, 0.0], + "a__c": [0.0, 0.0, 1.0, 1.0], + "b__a": [1.0, 0.0, 0.0, 1.0], + "b__nan": [0.0, 1.0, 1.0, 0.0], + }, + ), + ), + ], + ids=[ + "all columns", + "one column", + "multiple columns", + "single underscore counterexample", + "name conflict", + "column with nans", ], - ids=["all columns", "one column", "multiple columns"], ) def test_should_return_transformed_table( self, @@ -145,7 +272,7 @@ def test_should_return_transformed_table( assert OneHotEncoder().fit_and_transform(table, column_names) == expected def test_should_not_change_original_table(self) -> None: - table = Table.from_dict( + table = Table( { "col1": ["a", "b", "c"], }, @@ -153,7 +280,7 @@ def test_should_not_change_original_table(self) -> None: OneHotEncoder().fit_and_transform(table) - expected = Table.from_dict( + expected = Table( { "col1": ["a", "b", "c"], }, @@ -161,13 +288,52 @@ def test_should_not_change_original_table(self) -> None: assert table == expected + def test_get_names_of_added_columns(self) -> None: + transformer = OneHotEncoder() + with pytest.raises(TransformerNotFittedError): + transformer.get_names_of_added_columns() + + table = Table( + {"a__b": ["c", "d"], "a": ["b__c", "d"], "b": ["a", float("nan")]}, + ) + added_columns = ["a__b__c", "a__b__d", "a__b__c#2", "a__d", "b__a", "b__nan"] + + transformer = transformer.fit(table, None) + assert transformer.get_names_of_added_columns() == added_columns + + def test_get_names_of_changed_columns(self) -> None: + transformer = OneHotEncoder() + with pytest.raises(TransformerNotFittedError): + transformer.get_names_of_changed_columns() + + table = Table( + { + "a": ["b"], + }, + ) + transformer = transformer.fit(table, None) + assert transformer.get_names_of_changed_columns() == [] + + def test_get_names_of_removed_columns(self) -> None: + transformer = OneHotEncoder() + with pytest.raises(TransformerNotFittedError): + transformer.get_names_of_removed_columns() + + table = Table( + { + "a": ["b"], + }, + ) + transformer = transformer.fit(table, None) + assert transformer.get_names_of_removed_columns() == ["a"] + class TestInverseTransform: @pytest.mark.parametrize( ("table_to_fit", "column_names", "table_to_transform"), [ ( - Table.from_dict( + Table( { "a": [1.0, 0.0, 0.0, 0.0], "b": ["a", "b", "b", "c"], @@ -175,7 +341,7 @@ class TestInverseTransform: }, ), ["b"], - Table.from_dict( + Table( { "a": [1.0, 0.0, 0.0, 0.0], "b": ["a", "b", "b", "c"], @@ -184,7 +350,7 @@ class TestInverseTransform: ), ), ( - Table.from_dict( + Table( { "a": [1.0, 0.0, 0.0, 0.0], "b": ["a", "b", "b", "c"], @@ -192,7 +358,7 @@ class TestInverseTransform: }, ), ["b"], - Table.from_dict( + Table( { "c": [0.0, 0.0, 0.0, 1.0], "b": ["a", "b", "b", "c"], @@ -201,7 +367,7 @@ class TestInverseTransform: ), ), ( - Table.from_dict( + Table( { "a": [1.0, 0.0, 0.0, 0.0], "b": ["a", "b", "b", "c"], @@ -209,7 +375,7 @@ class TestInverseTransform: }, ), ["b", "bb"], - Table.from_dict( + Table( { "a": [1.0, 0.0, 0.0, 0.0], "b": ["a", "b", "b", "c"], @@ -217,11 +383,13 @@ class TestInverseTransform: }, ), ), + (Table({"a": ["a", "b", "b", float("nan")]}), ["a"], Table({"a": ["a", "b", "b", float("nan")]})), ], ids=[ "same table to fit and transform", "different tables to fit and transform", "one column name is a prefix of another column name", + "column of table contains nan", ], ) def test_should_return_original_table( @@ -235,34 +403,35 @@ def test_should_return_original_table( result = transformer.inverse_transform(transformer.transform(table_to_transform)) # This checks whether the columns are in the same order - assert result.get_column_names() == table_to_transform.get_column_names() + assert result.column_names == table_to_transform.column_names # This is subsumed by the next assertion, but we get a better error message assert result.schema == table_to_transform.schema assert result == table_to_transform def test_should_not_change_transformed_table(self) -> None: - table = Table.from_dict( + table = Table( { - "col1": ["a", "b", "b", "c"], + "col1": ["a", "b", "b", "c", float("nan")], }, ) - transformer = OneHotEncoder().fit(table) + transformer = OneHotEncoder().fit(table, None) transformed_table = transformer.transform(table) transformer.inverse_transform(transformed_table) - expected = Table.from_dict( + expected = Table( { - "col1_a": [1.0, 0.0, 0.0, 0.0], - "col1_b": [0.0, 1.0, 1.0, 0.0], - "col1_c": [0.0, 0.0, 0.0, 1.0], + "col1__a": [1.0, 0.0, 0.0, 0.0, 0.0], + "col1__b": [0.0, 1.0, 1.0, 0.0, 0.0], + "col1__c": [0.0, 0.0, 0.0, 1.0, 0.0], + "col1__nan": [0.0, 0.0, 0.0, 0.0, 1.0], }, ) assert transformed_table == expected def test_should_raise_if_not_fitted(self) -> None: - table = Table.from_dict( + table = Table( { "a": [1.0, 0.0, 0.0, 0.0], "b": [0.0, 1.0, 1.0, 0.0], @@ -272,5 +441,30 @@ def test_should_raise_if_not_fitted(self) -> None: transformer = OneHotEncoder() - with pytest.raises(TransformerNotFittedError): + with pytest.raises(TransformerNotFittedError, match=r"The transformer has not been fitted yet."): transformer.inverse_transform(table) + + def test_should_raise_if_column_not_found(self) -> None: + with pytest.raises(UnknownColumnNameError, match=r"Could not find column\(s\) 'col1__one, col1__two'"): + OneHotEncoder().fit(Table({"col1": ["one", "two"]}), ["col1"]).inverse_transform( + Table({"col1": [1.0, 0.0]}), + ) + + def test_should_raise_if_table_contains_non_numerical_data(self) -> None: + with pytest.raises( + NonNumericColumnError, + match=( + r"Tried to do a numerical operation on one or multiple non-numerical columns: \n\['col1__one'," + r" 'col1__two'\]" + ), + ): + OneHotEncoder().fit(Table({"col1": ["one", "two"]}), ["col1"]).inverse_transform( + Table({"col1__one": ["1", "null"], "col1__two": ["2", "ok"]}), + ) + + def test_should_raise_if_table_contains_no_rows(self) -> None: + with pytest.raises( + ValueError, + match=r"The OneHotEncoder cannot inverse transform the table because it contains 0 rows", + ): + OneHotEncoder().fit(Table({"col1": ["one"]}), ["col1"]).inverse_transform(Table({"col1__one": []})) diff --git a/tests/safeds/data/tabular/transformation/test_range_scaler.py b/tests/safeds/data/tabular/transformation/test_range_scaler.py new file mode 100644 index 000000000..5d8e1dde6 --- /dev/null +++ b/tests/safeds/data/tabular/transformation/test_range_scaler.py @@ -0,0 +1,317 @@ +import pytest +from safeds.data.tabular.containers import Table +from safeds.data.tabular.transformation import RangeScaler +from safeds.exceptions import NonNumericColumnError, TransformerNotFittedError, UnknownColumnNameError + + +class TestInit: + def test_should_raise_value_error(self) -> None: + with pytest.raises(ValueError, match='Parameter "maximum" must be higher than parameter "minimum".'): + _ = RangeScaler(minimum=10, maximum=0) + + +class TestFit: + def test_should_raise_if_column_not_found(self) -> None: + table = Table( + { + "col1": [0.0, 5.0, 10.0], + }, + ) + + with pytest.raises(UnknownColumnNameError, match=r"Could not find column\(s\) 'col2, col3'"): + RangeScaler().fit(table, ["col2", "col3"]) + + def test_should_raise_if_table_contains_non_numerical_data(self) -> None: + with pytest.raises( + NonNumericColumnError, + match=r"Tried to do a numerical operation on one or multiple non-numerical columns: \n\['col1', 'col2'\]", + ): + RangeScaler().fit(Table({"col1": ["a", "b"], "col2": [1, "c"]}), ["col1", "col2"]) + + def test_should_raise_if_table_contains_no_rows(self) -> None: + with pytest.raises(ValueError, match=r"The RangeScaler cannot be fitted because the table contains 0 rows"): + RangeScaler().fit(Table({"col1": []}), ["col1"]) + + def test_should_not_change_original_transformer(self) -> None: + table = Table( + { + "col1": [0.0, 5.0, 10.0], + }, + ) + + transformer = RangeScaler() + transformer.fit(table, None) + + assert transformer._wrapped_transformer is None + assert transformer._column_names is None + + +class TestTransform: + def test_should_raise_if_column_not_found(self) -> None: + table_to_fit = Table( + { + "col1": [0.0, 5.0, 10.0], + "col2": [5.0, 50.0, 100.0], + }, + ) + + transformer = RangeScaler().fit(table_to_fit, None) + + table_to_transform = Table( + { + "col3": ["a", "b", "c"], + }, + ) + + with pytest.raises(UnknownColumnNameError, match=r"Could not find column\(s\) 'col1, col2'"): + transformer.transform(table_to_transform) + + def test_should_raise_if_not_fitted(self) -> None: + table = Table( + { + "col1": [0.0, 5.0, 10.0], + }, + ) + + transformer = RangeScaler() + + with pytest.raises(TransformerNotFittedError): + transformer.transform(table) + + def test_should_raise_if_table_contains_non_numerical_data(self) -> None: + with pytest.raises( + NonNumericColumnError, + match=r"Tried to do a numerical operation on one or multiple non-numerical columns: \n\['col1', 'col2'\]", + ): + RangeScaler().fit(Table({"col1": [1, 2, 3], "col2": [2, 3, 4]}), ["col1", "col2"]).transform( + Table({"col1": ["a", "b", "c"], "col2": ["c", "d", "e"]}), + ) + + def test_should_raise_if_table_contains_no_rows(self) -> None: + with pytest.raises(ValueError, match=r"The RangeScaler cannot transform the table because it contains 0 rows"): + RangeScaler().fit(Table({"col1": [1, 2, 3]}), ["col1"]).transform(Table({"col1": []})) + + +class TestIsFitted: + def test_should_return_false_before_fitting(self) -> None: + transformer = RangeScaler() + assert not transformer.is_fitted() + + def test_should_return_true_after_fitting(self) -> None: + table = Table( + { + "col1": [0.0, 5.0, 10.0], + }, + ) + + transformer = RangeScaler() + fitted_transformer = transformer.fit(table, None) + assert fitted_transformer.is_fitted() + + +class TestFitAndTransform: + @pytest.mark.parametrize( + ("table", "column_names", "expected"), + [ + ( + Table( + { + "col1": [0.0, 5.0, 5.0, 10.0], + }, + ), + None, + Table( + { + "col1": [0.0, 0.5, 0.5, 1.0], + }, + ), + ), + ( + Table( + { + "col1": [0.0, 5.0, 5.0, 10.0], + "col2": [0.0, 5.0, 5.0, 10.0], + }, + ), + ["col1"], + Table( + { + "col1": [0.0, 0.5, 0.5, 1.0], + "col2": [0.0, 5.0, 5.0, 10.0], + }, + ), + ), + ], + ids=["one_column", "two_columns"], + ) + def test_should_return_transformed_table( + self, + table: Table, + column_names: list[str] | None, + expected: Table, + ) -> None: + assert RangeScaler().fit_and_transform(table, column_names) == expected + + @pytest.mark.parametrize( + ("table", "column_names", "expected"), + [ + ( + Table( + { + "col1": [0.0, 5.0, 5.0, 10.0], + }, + ), + None, + Table( + { + "col1": [-10.0, 0.0, 0.0, 10.0], + }, + ), + ), + ( + Table( + { + "col1": [0.0, 5.0, 5.0, 10.0], + "col2": [0.0, 5.0, 5.0, 10.0], + }, + ), + ["col1"], + Table( + { + "col1": [-10.0, 0.0, 0.0, 10.0], + "col2": [0.0, 5.0, 5.0, 10.0], + }, + ), + ), + ], + ids=["one_column", "two_columns"], + ) + def test_should_return_transformed_table_with_correct_range( + self, + table: Table, + column_names: list[str] | None, + expected: Table, + ) -> None: + assert RangeScaler(minimum=-10.0, maximum=10.0).fit_and_transform(table, column_names) == expected + + def test_should_not_change_original_table(self) -> None: + table = Table( + { + "col1": [0.0, 5.0, 10.0], + }, + ) + + RangeScaler().fit_and_transform(table) + + expected = Table( + { + "col1": [0.0, 5.0, 10.0], + }, + ) + + assert table == expected + + def test_get_names_of_added_columns(self) -> None: + transformer = RangeScaler() + with pytest.raises(TransformerNotFittedError): + transformer.get_names_of_added_columns() + + table = Table( + { + "a": [0.0], + }, + ) + transformer = transformer.fit(table, None) + assert transformer.get_names_of_added_columns() == [] + + def test_get_names_of_changed_columns(self) -> None: + transformer = RangeScaler() + with pytest.raises(TransformerNotFittedError): + transformer.get_names_of_changed_columns() + table = Table( + { + "a": [0.0], + }, + ) + transformer = transformer.fit(table, None) + assert transformer.get_names_of_changed_columns() == ["a"] + + def test_get_names_of_removed_columns(self) -> None: + transformer = RangeScaler() + with pytest.raises(TransformerNotFittedError): + transformer.get_names_of_removed_columns() + + table = Table( + { + "a": [0.0], + }, + ) + transformer = transformer.fit(table, None) + assert transformer.get_names_of_removed_columns() == [] + + +class TestInverseTransform: + @pytest.mark.parametrize( + "table", + [ + Table( + { + "col1": [0.0, 5.0, 5.0, 10.0], + }, + ), + ], + ) + def test_should_return_original_table(self, table: Table) -> None: + transformer = RangeScaler().fit(table, None) + + assert transformer.inverse_transform(transformer.transform(table)) == table + + def test_should_not_change_transformed_table(self) -> None: + table = Table( + { + "col1": [0.0, 0.5, 1.0], + }, + ) + + transformer = RangeScaler().fit(table, None) + transformed_table = transformer.transform(table) + transformer.inverse_transform(transformed_table) + + expected = Table( + { + "col1": [0.0, 0.5, 1.0], + }, + ) + + assert transformed_table == expected + + def test_should_raise_if_not_fitted(self) -> None: + table = Table( + { + "col1": [0.0, 5.0, 5.0, 10.0], + }, + ) + + transformer = RangeScaler() + + with pytest.raises(TransformerNotFittedError, match=r"The transformer has not been fitted yet."): + transformer.inverse_transform(table) + + def test_should_raise_if_column_not_found(self) -> None: + with pytest.raises(UnknownColumnNameError, match=r"Could not find column\(s\) 'col1, col2'"): + RangeScaler().fit(Table({"col1": [1, 2, 3], "col2": [2, 3, 4]}), ["col1", "col2"]).inverse_transform( + Table({"col3": [1, 2, 3]}), + ) + + def test_should_raise_if_table_contains_non_numerical_data(self) -> None: + with pytest.raises( + NonNumericColumnError, + match=r"Tried to do a numerical operation on one or multiple non-numerical columns: \n\['col1', 'col2'\]", + ): + RangeScaler().fit(Table({"col1": [1, 2, 3], "col2": [2, 3, 4]}), ["col1", "col2"]).inverse_transform( + Table({"col1": ["1", "2", "three"], "col2": [1, 2, "four"]}), + ) + + def test_should_raise_if_table_contains_no_rows(self) -> None: + with pytest.raises(ValueError, match=r"The RangeScaler cannot transform the table because it contains 0 rows"): + RangeScaler().fit(Table({"col1": [1, 2, 3]}), ["col1"]).inverse_transform(Table({"col1": []})) diff --git a/tests/safeds/data/tabular/transformation/test_standard_scaler.py b/tests/safeds/data/tabular/transformation/test_standard_scaler.py new file mode 100644 index 000000000..e11069d5b --- /dev/null +++ b/tests/safeds/data/tabular/transformation/test_standard_scaler.py @@ -0,0 +1,286 @@ +import pytest +from safeds.data.tabular.containers import Table +from safeds.data.tabular.transformation import StandardScaler +from safeds.exceptions import NonNumericColumnError, TransformerNotFittedError, UnknownColumnNameError + +from tests.helpers import assert_that_tables_are_close + + +class TestFit: + def test_should_raise_if_column_not_found(self) -> None: + table = Table( + { + "col1": [0.0, 5.0, 10.0], + }, + ) + + with pytest.raises(UnknownColumnNameError, match=r"Could not find column\(s\) 'col2, col3'"): + StandardScaler().fit(table, ["col2", "col3"]) + + def test_should_raise_if_table_contains_non_numerical_data(self) -> None: + with pytest.raises( + NonNumericColumnError, + match=r"Tried to do a numerical operation on one or multiple non-numerical columns: \n\['col1', 'col2'\]", + ): + StandardScaler().fit( + Table({"col1": ["one", "two", "apple"], "col2": ["three", "four", "banana"]}), + ["col1", "col2"], + ) + + def test_should_raise_if_table_contains_no_rows(self) -> None: + with pytest.raises(ValueError, match=r"The StandardScaler cannot be fitted because the table contains 0 rows"): + StandardScaler().fit(Table({"col1": []}), ["col1"]) + + def test_should_not_change_original_transformer(self) -> None: + table = Table( + { + "col1": [0.0, 5.0, 10.0], + }, + ) + + transformer = StandardScaler() + transformer.fit(table, None) + + assert transformer._wrapped_transformer is None + assert transformer._column_names is None + + +class TestTransform: + def test_should_raise_if_column_not_found(self) -> None: + table_to_fit = Table( + { + "col1": [0.0, 5.0, 10.0], + "col2": [5.0, 50.0, 100.0], + }, + ) + + transformer = StandardScaler().fit(table_to_fit, None) + + table_to_transform = Table( + { + "col3": ["a", "b", "c"], + }, + ) + + with pytest.raises(UnknownColumnNameError, match=r"Could not find column\(s\) 'col1, col2'"): + transformer.transform(table_to_transform) + + def test_should_raise_if_not_fitted(self) -> None: + table = Table( + { + "col1": [0.0, 5.0, 10.0], + }, + ) + + transformer = StandardScaler() + + with pytest.raises(TransformerNotFittedError, match=r"The transformer has not been fitted yet."): + transformer.transform(table) + + def test_should_raise_if_table_contains_non_numerical_data(self) -> None: + with pytest.raises( + NonNumericColumnError, + match=r"Tried to do a numerical operation on one or multiple non-numerical columns: \n\['col1', 'col2'\]", + ): + StandardScaler().fit(Table({"col1": [1, 2, 3], "col2": [2, 3, 4]}), ["col1", "col2"]).transform( + Table({"col1": ["a", "b", "c"], "col2": ["b", "c", "e"]}), + ) + + def test_should_raise_if_table_contains_no_rows(self) -> None: + with pytest.raises( + ValueError, + match=r"The StandardScaler cannot transform the table because it contains 0 rows", + ): + StandardScaler().fit(Table({"col1": [1, 2, 3]}), ["col1"]).transform(Table({"col1": []})) + + +class TestIsFitted: + def test_should_return_false_before_fitting(self) -> None: + transformer = StandardScaler() + assert not transformer.is_fitted() + + def test_should_return_true_after_fitting(self) -> None: + table = Table( + { + "col1": [0.0, 5.0, 10.0], + }, + ) + + transformer = StandardScaler() + fitted_transformer = transformer.fit(table, None) + assert fitted_transformer.is_fitted() + + +class TestFitAndTransformOnMultipleTables: + @pytest.mark.parametrize( + ("fit_and_transform_table", "only_transform_table", "column_names", "expected_1", "expected_2"), + [ + ( + Table( + { + "col1": [0.0, 0.0, 1.0, 1.0], + "col2": [0.0, 0.0, 1.0, 1.0], + }, + ), + Table( + { + "col1": [2], + "col2": [2], + }, + ), + None, + Table( + { + "col1": [-1.0, -1.0, 1.0, 1.0], + "col2": [-1.0, -1.0, 1.0, 1.0], + }, + ), + Table( + { + "col1": [3.0], + "col2": [3.0], + }, + ), + ), + ], + ids=["two_columns"], + ) + def test_should_return_transformed_tables( + self, + fit_and_transform_table: Table, + only_transform_table: Table, + column_names: list[str] | None, + expected_1: Table, + expected_2: Table, + ) -> None: + s = StandardScaler().fit(fit_and_transform_table, column_names) + assert s.fit_and_transform(fit_and_transform_table, column_names) == expected_1 + assert s.transform(only_transform_table) == expected_2 + + +class TestFitAndTransform: + def test_should_not_change_original_table(self) -> None: + table = Table( + { + "col1": [0.0, 5.0, 10.0], + }, + ) + + StandardScaler().fit_and_transform(table) + + expected = Table( + { + "col1": [0.0, 5.0, 10.0], + }, + ) + + assert table == expected + + def test_get_names_of_added_columns(self) -> None: + transformer = StandardScaler() + with pytest.raises(TransformerNotFittedError): + transformer.get_names_of_added_columns() + + table = Table( + { + "a": [0.0], + }, + ) + transformer = transformer.fit(table, None) + assert transformer.get_names_of_added_columns() == [] + + def test_get_names_of_changed_columns(self) -> None: + transformer = StandardScaler() + with pytest.raises(TransformerNotFittedError): + transformer.get_names_of_changed_columns() + table = Table( + { + "a": [0.0], + }, + ) + transformer = transformer.fit(table, None) + assert transformer.get_names_of_changed_columns() == ["a"] + + def test_get_names_of_removed_columns(self) -> None: + transformer = StandardScaler() + with pytest.raises(TransformerNotFittedError): + transformer.get_names_of_removed_columns() + + table = Table( + { + "a": [0.0], + }, + ) + transformer = transformer.fit(table, None) + assert transformer.get_names_of_removed_columns() == [] + + +class TestInverseTransform: + @pytest.mark.parametrize( + "table", + [ + Table( + { + "col1": [0.0, 5.0, 5.0, 10.0], + }, + ), + ], + ids=["one_column"], + ) + def test_should_return_original_table(self, table: Table) -> None: + transformer = StandardScaler().fit(table, None) + + assert transformer.inverse_transform(transformer.transform(table)) == table + + def test_should_not_change_transformed_table(self) -> None: + table = Table( + { + "col1": [0.0, 0.5, 1.0], + }, + ) + + transformer = StandardScaler().fit(table, None) + transformed_table = transformer.transform(table) + transformed_table = transformer.inverse_transform(transformed_table) + + expected = Table( + { + "col1": [0.0, 0.5, 1.0], + }, + ) + + assert_that_tables_are_close(transformed_table, expected) + + def test_should_raise_if_not_fitted(self) -> None: + table = Table( + { + "col1": [0.0, 5.0, 5.0, 10.0], + }, + ) + + transformer = StandardScaler() + + with pytest.raises(TransformerNotFittedError, match=r"The transformer has not been fitted yet."): + transformer.inverse_transform(table) + + def test_should_raise_if_column_not_found(self) -> None: + with pytest.raises(UnknownColumnNameError, match=r"Could not find column\(s\) 'col1, col2'"): + StandardScaler().fit(Table({"col1": [1, 2, 4], "col2": [2, 3, 4]}), ["col1", "col2"]).inverse_transform( + Table({"col3": [0, 1, 2]}), + ) + + def test_should_raise_if_table_contains_non_numerical_data(self) -> None: + with pytest.raises( + NonNumericColumnError, + match=r"Tried to do a numerical operation on one or multiple non-numerical columns: \n\['col1', 'col2'\]", + ): + StandardScaler().fit(Table({"col1": [1, 2, 4], "col2": [2, 3, 4]}), ["col1", "col2"]).inverse_transform( + Table({"col1": ["one", "two", "apple"], "col2": ["three", "four", "banana"]}), + ) + + def test_should_raise_if_table_contains_no_rows(self) -> None: + with pytest.raises( + ValueError, + match=r"The StandardScaler cannot transform the table because it contains 0 rows", + ): + StandardScaler().fit(Table({"col1": [1, 2, 4]}), ["col1"]).inverse_transform(Table({"col1": []})) diff --git a/tests/safeds/data/tabular/typing/_schema/test__str__.py b/tests/safeds/data/tabular/typing/_schema/test__str__.py deleted file mode 100644 index 9bdf89652..000000000 --- a/tests/safeds/data/tabular/typing/_schema/test__str__.py +++ /dev/null @@ -1,6 +0,0 @@ -from safeds.data.tabular.containers import Table - - -def test__str__() -> None: - table = Table.from_dict({"col1": ["col1_1"], "col2": [1]}) - assert str(table.schema) == "TableSchema:\nColumn Count: 2\nColumns:\n col1: String\n col2: Integer\n" diff --git a/tests/safeds/data/tabular/typing/_schema/test_get_column_index_by_name.py b/tests/safeds/data/tabular/typing/_schema/test_get_column_index_by_name.py deleted file mode 100644 index 3d445e8d0..000000000 --- a/tests/safeds/data/tabular/typing/_schema/test_get_column_index_by_name.py +++ /dev/null @@ -1,7 +0,0 @@ -from safeds.data.tabular.containers import Table - - -def test_get_column_index_by_name() -> None: - table = Table.from_dict({"col1": [1], "col2": [2]}) - assert table.schema._get_column_index_by_name("col1") == 0 - assert table.schema._get_column_index_by_name("col2") == 1 diff --git a/tests/safeds/data/tabular/typing/_schema/test_get_column_type.py b/tests/safeds/data/tabular/typing/_schema/test_get_column_type.py deleted file mode 100644 index 7fca57641..000000000 --- a/tests/safeds/data/tabular/typing/_schema/test_get_column_type.py +++ /dev/null @@ -1,10 +0,0 @@ -from safeds.data.tabular.containers import Table -from safeds.data.tabular.typing import Integer - -from tests.helpers import resolve_resource_path - - -def test_get_type_of_column() -> None: - table = Table.from_json_file(resolve_resource_path("test_schema_table.json")) - table_column_type = table.schema.get_type_of_column("A") - assert table_column_type == Integer() diff --git a/tests/safeds/data/tabular/typing/_schema/test_has_column.py b/tests/safeds/data/tabular/typing/_schema/test_has_column.py deleted file mode 100644 index 86dea84df..000000000 --- a/tests/safeds/data/tabular/typing/_schema/test_has_column.py +++ /dev/null @@ -1,15 +0,0 @@ -from safeds.data.tabular.containers import Table - -from tests.helpers import resolve_resource_path - - -def test_has_column_true() -> None: - table = Table.from_json_file(resolve_resource_path("test_schema_table.json")) - - assert table.schema.has_column("A") - - -def test_has_column_false() -> None: - table = Table.from_json_file(resolve_resource_path("test_schema_table.json")) - - assert not table.schema.has_column("C") diff --git a/tests/safeds/data/tabular/typing/_schema/test_table_equals.py b/tests/safeds/data/tabular/typing/_schema/test_table_equals.py deleted file mode 100644 index 1da7b049d..000000000 --- a/tests/safeds/data/tabular/typing/_schema/test_table_equals.py +++ /dev/null @@ -1,28 +0,0 @@ -from safeds.data.tabular.containers import Table -from safeds.data.tabular.typing import Integer, RealNumber, Schema - -from tests.helpers import resolve_resource_path - - -def test_table_equals_valid() -> None: - table = Table.from_json_file(resolve_resource_path("test_schema_table.json")) - schema_expected = Schema( - { - "A": Integer(), - "B": Integer(), - }, - ) - - assert table.schema == schema_expected - - -def test_table_equals_invalid() -> None: - table = Table.from_json_file(resolve_resource_path("test_schema_table.json")) - schema_not_expected = Schema( - { - "A": RealNumber(), - "C": Integer(), - }, - ) - - assert table.schema != schema_not_expected diff --git a/tests/safeds/data/tabular/typing/test_column_type.py b/tests/safeds/data/tabular/typing/test_column_type.py index 05c614b92..5ad58915b 100644 --- a/tests/safeds/data/tabular/typing/test_column_type.py +++ b/tests/safeds/data/tabular/typing/test_column_type.py @@ -10,7 +10,47 @@ ) -class TestColumnType: +class TestFromNumpyDataType: + # Test cases taken from https://numpy.org/doc/stable/reference/arrays.scalars.html#scalars + @pytest.mark.parametrize( + ("data_type", "expected"), + [ + # Boolean + (np.dtype(np.bool_), Boolean()), + # Number + (np.dtype(np.half), RealNumber()), + (np.dtype(np.single), RealNumber()), + (np.dtype(np.float_), RealNumber()), + (np.dtype(np.longfloat), RealNumber()), + # Int + (np.dtype(np.byte), Integer()), + (np.dtype(np.short), Integer()), + (np.dtype(np.intc), Integer()), + (np.dtype(np.int_), Integer()), + (np.dtype(np.longlong), Integer()), + (np.dtype(np.ubyte), Integer()), + (np.dtype(np.ushort), Integer()), + (np.dtype(np.uintc), Integer()), + (np.dtype(np.uint), Integer()), + (np.dtype(np.ulonglong), Integer()), + # String + (np.dtype(np.str_), String()), + (np.dtype(np.unicode_), String()), + (np.dtype(np.object_), String()), + (np.dtype(np.datetime64), String()), + (np.dtype(np.timedelta64), String()), + ], + ids=repr, + ) + def test_should_create_column_type_from_numpy_data_type(self, data_type: np.dtype, expected: ColumnType) -> None: + assert ColumnType._from_numpy_data_type(data_type) == expected + + def test_should_raise_if_data_type_is_not_supported(self) -> None: + with pytest.raises(NotImplementedError): + ColumnType._from_numpy_data_type(np.dtype(np.void)) + + +class TestRepr: @pytest.mark.parametrize( ("column_type", "expected"), [ @@ -27,9 +67,11 @@ class TestColumnType: ], ids=repr, ) - def test_repr(self, column_type: ColumnType, expected: str) -> None: + def test_should_create_a_printable_representation(self, column_type: ColumnType, expected: str) -> None: assert repr(column_type) == expected + +class TestIsNullable: @pytest.mark.parametrize( ("column_type", "expected"), [ @@ -46,9 +88,11 @@ def test_repr(self, column_type: ColumnType, expected: str) -> None: ], ids=repr, ) - def test_is_nullable(self, column_type: ColumnType, expected: bool) -> None: + def test_should_return_whether_the_column_type_is_nullable(self, column_type: ColumnType, expected: bool) -> None: assert column_type.is_nullable() == expected + +class TestIsNumeric: @pytest.mark.parametrize( ("column_type", "expected"), [ @@ -65,39 +109,5 @@ def test_is_nullable(self, column_type: ColumnType, expected: bool) -> None: ], ids=repr, ) - def test_is_numeric(self, column_type: ColumnType, expected: bool) -> None: + def test_should_return_whether_the_column_type_is_numeric(self, column_type: ColumnType, expected: bool) -> None: assert column_type.is_numeric() == expected - - # Test cases taken from https://numpy.org/doc/stable/reference/arrays.scalars.html#scalars - @pytest.mark.parametrize( - ("dtype", "expected"), - [ - # Boolean - (np.dtype(np.bool_), Boolean()), - # Number - (np.dtype(np.half), RealNumber()), - (np.dtype(np.single), RealNumber()), - (np.dtype(np.float_), RealNumber()), - (np.dtype(np.longfloat), RealNumber()), - # Int - (np.dtype(np.byte), Integer()), - (np.dtype(np.short), Integer()), - (np.dtype(np.intc), Integer()), - (np.dtype(np.int_), Integer()), - (np.dtype(np.longlong), Integer()), - (np.dtype(np.ubyte), Integer()), - (np.dtype(np.ushort), Integer()), - (np.dtype(np.uintc), Integer()), - (np.dtype(np.uint), Integer()), - (np.dtype(np.ulonglong), Integer()), - # String - (np.dtype(np.str_), String()), - (np.dtype(np.unicode_), String()), - (np.dtype(np.object_), String()), - (np.dtype(np.datetime64), String()), - (np.dtype(np.timedelta64), String()), - ], - ids=repr, - ) - def test_from_numpy_dtype(self, dtype: np.dtype, expected: ColumnType) -> None: - assert ColumnType._from_numpy_dtype(dtype) == expected diff --git a/tests/safeds/data/tabular/typing/test_schema.py b/tests/safeds/data/tabular/typing/test_schema.py new file mode 100644 index 000000000..f4e827353 --- /dev/null +++ b/tests/safeds/data/tabular/typing/test_schema.py @@ -0,0 +1,256 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING + +import pandas as pd +import pytest +from safeds.data.tabular.typing import Boolean, ColumnType, Integer, RealNumber, Schema, String +from safeds.exceptions import UnknownColumnNameError + +if TYPE_CHECKING: + from typing import Any + + +class TestFromPandasDataFrame: + @pytest.mark.parametrize( + ("dataframe", "expected"), + [ + ( + pd.DataFrame({"A": [True, False, True]}), + Schema({"A": Boolean()}), + ), + ( + pd.DataFrame({"A": [1, 2, 3]}), + Schema({"A": Integer()}), + ), + ( + pd.DataFrame({"A": [1.0, 2.0, 3.0]}), + Schema({"A": RealNumber()}), + ), + ( + pd.DataFrame({"A": ["a", "b", "c"]}), + Schema({"A": String()}), + ), + ( + pd.DataFrame({"A": [1, 2.0, "a", True]}), + Schema({"A": String()}), + ), + ( + pd.DataFrame({"A": [1, 2, 3], "B": ["a", "b", "c"]}), + Schema({"A": Integer(), "B": String()}), + ), + ], + ids=[ + "integer", + "real number", + "string", + "boolean", + "mixed", + "multiple columns", + ], + ) + def test_should_create_schema_from_pandas_dataframe(self, dataframe: pd.DataFrame, expected: Schema) -> None: + assert Schema._from_pandas_dataframe(dataframe) == expected + + +class TestRepr: + @pytest.mark.parametrize( + ("schema", "expected"), + [ + (Schema({}), "Schema({})"), + (Schema({"A": Integer()}), "Schema({'A': Integer})"), + (Schema({"A": Integer(), "B": String()}), "Schema({\n 'A': Integer,\n 'B': String\n})"), + ], + ids=[ + "empty", + "single column", + "multiple columns", + ], + ) + def test_should_create_a_string_representation(self, schema: Schema, expected: str) -> None: + assert repr(schema) == expected + + +class TestStr: + @pytest.mark.parametrize( + ("schema", "expected"), + [ + (Schema({}), "{}"), + (Schema({"A": Integer()}), "{'A': Integer}"), + (Schema({"A": Integer(), "B": String()}), "{\n 'A': Integer,\n 'B': String\n}"), + ], + ids=[ + "empty", + "single column", + "multiple columns", + ], + ) + def test_should_create_a_string_representation(self, schema: Schema, expected: str) -> None: + assert str(schema) == expected + + +class TestEq: + @pytest.mark.parametrize( + ("schema1", "schema2", "expected"), + [ + (Schema({}), Schema({}), True), + (Schema({"col1": Integer()}), Schema({"col1": Integer()}), True), + (Schema({"col1": Integer()}), Schema({"col1": String()}), False), + (Schema({"col1": Integer()}), Schema({"col2": Integer()}), False), + ( + Schema({"col1": Integer(), "col2": String()}), + Schema({"col2": String(), "col1": Integer()}), + True, + ), + ], + ids=[ + "empty", + "same name and type", + "same name but different type", + "different name but same type", + "flipped columns", + ], + ) + def test_should_return_whether_two_schema_are_equal(self, schema1: Schema, schema2: Schema, expected: bool) -> None: + assert (schema1.__eq__(schema2)) == expected + + @pytest.mark.parametrize( + ("schema", "other"), + [ + (Schema({"col1": Integer()}), None), + (Schema({"col1": Integer()}), {"col1": Integer()}), + ], + ) + def test_should_return_not_implemented_if_other_is_not_schema(self, schema: Schema, other: Any) -> None: + assert (schema.__eq__(other)) is NotImplemented + + +class TestHash: + @pytest.mark.parametrize( + ("schema1", "schema2"), + [ + (Schema({}), Schema({})), + (Schema({"col1": Integer()}), Schema({"col1": Integer()})), + ], + ids=[ + "empty", + "one column", + ], + ) + def test_should_return_same_hash_for_equal_schemas(self, schema1: Schema, schema2: Schema) -> None: + assert hash(schema1) == hash(schema2) + + @pytest.mark.parametrize( + ("schema1", "schema2"), + [ + (Schema({"col1": Integer()}), Schema({"col1": String()})), + (Schema({"col1": Integer()}), Schema({"col2": Integer()})), + ], + ids=[ + "same name but different type", + "different name but same type", + ], + ) + def test_should_return_different_hash_for_unequal_schemas(self, schema1: Schema, schema2: Schema) -> None: + assert hash(schema1) != hash(schema2) + + +class TestHasColumn: + @pytest.mark.parametrize( + ("schema", "column_name", "expected"), + [ + (Schema({}), "A", False), + (Schema({"A": Integer()}), "A", True), + (Schema({"A": Integer()}), "B", False), + ], + ids=[ + "empty", + "column exists", + "column does not exist", + ], + ) + def test_should_return_whether_column_exists(self, schema: Schema, column_name: str, expected: bool) -> None: + assert schema.has_column(column_name) == expected + + +class TestGetTypeOfColumn: + @pytest.mark.parametrize( + ("schema", "column_name", "expected"), + [ + (Schema({"A": Integer()}), "A", Integer()), + (Schema({"A": Integer(), "B": String()}), "B", String()), + ], + ids=[ + "one column", + "two columns", + ], + ) + def test_should_return_type_of_existing_column( + self, + schema: Schema, + column_name: str, + expected: ColumnType, + ) -> None: + assert schema.get_column_type(column_name) == expected + + def test_should_raise_if_column_does_not_exist(self) -> None: + schema = Schema({"A": Integer()}) + with pytest.raises(UnknownColumnNameError): + schema.get_column_type("B") + + +class TestGetColumnNames: + @pytest.mark.parametrize( + ("schema", "expected"), + [ + (Schema({}), []), + (Schema({"A": Integer()}), ["A"]), + (Schema({"A": Integer(), "B": RealNumber()}), ["A", "B"]), + ], + ids=[ + "empty", + "single column", + "multiple columns", + ], + ) + def test_should_return_column_names(self, schema: Schema, expected: list[str]) -> None: + assert schema.column_names == expected + + +class TestToDict: + @pytest.mark.parametrize( + ("schema", "expected"), + [ + (Schema({}), {}), + (Schema({"A": Integer()}), {"A": Integer()}), + (Schema({"A": Integer(), "B": String()}), {"A": Integer(), "B": String()}), + ], + ids=[ + "empty", + "single column", + "multiple columns", + ], + ) + def test_should_return_dict_for_schema(self, schema: Schema, expected: str) -> None: + assert schema.to_dict() == expected + + +class TestReprMarkdown: + @pytest.mark.parametrize( + ("schema", "expected"), + [ + (Schema({}), "Empty Schema"), + (Schema({"A": Integer()}), "| Column Name | Column Type |\n| --- | --- |\n| A | Integer |"), + ( + Schema({"A": Integer(), "B": String()}), + "| Column Name | Column Type |\n| --- | --- |\n| A | Integer |\n| B | String |", + ), + ], + ids=[ + "empty", + "single column", + "multiple columns", + ], + ) + def test_should_create_a_string_representation(self, schema: Schema, expected: str) -> None: + assert schema._repr_markdown_() == expected diff --git a/tests/safeds/ml/regression/__init__.py b/tests/safeds/ml/classical/__init__.py similarity index 100% rename from tests/safeds/ml/regression/__init__.py rename to tests/safeds/ml/classical/__init__.py diff --git a/tests/safeds/ml/classical/classification/__init__.py b/tests/safeds/ml/classical/classification/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/safeds/ml/classical/classification/test_ada_boost.py b/tests/safeds/ml/classical/classification/test_ada_boost.py new file mode 100644 index 000000000..1ecec8f4c --- /dev/null +++ b/tests/safeds/ml/classical/classification/test_ada_boost.py @@ -0,0 +1,52 @@ +import pytest +from safeds.data.tabular.containers import Table, TaggedTable +from safeds.ml.classical.classification import AdaBoost + + +@pytest.fixture() +def training_set() -> TaggedTable: + table = Table({"col1": [1, 2, 3, 4], "col2": [1, 2, 3, 4]}) + return table.tag_columns(target_name="col1", feature_names=["col2"]) + + +class TestLearner: + def test_should_be_passed_to_fitted_model(self, training_set: TaggedTable) -> None: + learner = AdaBoost() + fitted_model = AdaBoost(learner=learner).fit(training_set) + assert fitted_model.learner == learner + + def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None: + learner = AdaBoost() + fitted_model = AdaBoost(learner=learner).fit(training_set) + assert fitted_model._wrapped_classifier is not None + assert isinstance(fitted_model._wrapped_classifier.estimator, type(learner._get_sklearn_classifier())) + + +class TestMaximumNumberOfLearners: + def test_should_be_passed_to_fitted_model(self, training_set: TaggedTable) -> None: + fitted_model = AdaBoost(maximum_number_of_learners=2).fit(training_set) + assert fitted_model.maximum_number_of_learners == 2 + + def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None: + fitted_model = AdaBoost(maximum_number_of_learners=2).fit(training_set) + assert fitted_model._wrapped_classifier is not None + assert fitted_model._wrapped_classifier.n_estimators == 2 + + def test_should_raise_if_less_than_or_equal_to_0(self) -> None: + with pytest.raises(ValueError, match="The parameter 'maximum_number_of_learners' has to be grater than 0."): + AdaBoost(maximum_number_of_learners=-1) + + +class TestLearningRate: + def test_should_be_passed_to_fitted_model(self, training_set: TaggedTable) -> None: + fitted_model = AdaBoost(learning_rate=2).fit(training_set) + assert fitted_model.learning_rate == 2 + + def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None: + fitted_model = AdaBoost(learning_rate=2).fit(training_set) + assert fitted_model._wrapped_classifier is not None + assert fitted_model._wrapped_classifier.learning_rate == 2 + + def test_should_raise_if_less_than_or_equal_to_0(self) -> None: + with pytest.raises(ValueError, match="The parameter 'learning_rate' has to be greater than 0."): + AdaBoost(learning_rate=-1) diff --git a/tests/safeds/ml/classical/classification/test_classifier.py b/tests/safeds/ml/classical/classification/test_classifier.py new file mode 100644 index 000000000..714fc8ed2 --- /dev/null +++ b/tests/safeds/ml/classical/classification/test_classifier.py @@ -0,0 +1,476 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING, Any + +import pytest +from safeds.data.tabular.containers import Table, TaggedTable +from safeds.exceptions import ( + DatasetContainsTargetError, + DatasetMissesDataError, + DatasetMissesFeaturesError, + MissingValuesColumnError, + ModelNotFittedError, + NonNumericColumnError, + UntaggedTableError, +) +from safeds.ml.classical.classification import ( + AdaBoost, + Classifier, + DecisionTree, + GradientBoosting, + KNearestNeighbors, + LogisticRegression, + RandomForest, + SupportVectorMachine, +) + +if TYPE_CHECKING: + from _pytest.fixtures import FixtureRequest + from sklearn.base import ClassifierMixin + + +def classifiers() -> list[Classifier]: + """ + Return the list of classifiers to test. + + After you implemented a new classifier, add it to this list to ensure its `fit` and `predict` method work as + expected. Place tests of methods that are specific to your classifier in a separate test file. + + Returns + ------- + classifiers : list[Classifier] + The list of classifiers to test. + """ + return [ + AdaBoost(), + DecisionTree(), + GradientBoosting(), + KNearestNeighbors(2), + LogisticRegression(), + RandomForest(), + SupportVectorMachine(), + ] + + +@pytest.fixture() +def valid_data() -> TaggedTable: + return Table( + { + "id": [1, 4], + "feat1": [2, 5], + "feat2": [3, 6], + "target": [0, 1], + }, + ).tag_columns(target_name="target", feature_names=["feat1", "feat2"]) + + +@pytest.mark.parametrize("classifier", classifiers(), ids=lambda x: x.__class__.__name__) +class TestFit: + def test_should_succeed_on_valid_data(self, classifier: Classifier, valid_data: TaggedTable) -> None: + classifier.fit(valid_data) + assert True # This asserts that the fit method succeeds + + def test_should_not_change_input_classifier(self, classifier: Classifier, valid_data: TaggedTable) -> None: + classifier.fit(valid_data) + assert not classifier.is_fitted() + + def test_should_not_change_input_table(self, classifier: Classifier, request: FixtureRequest) -> None: + valid_data = request.getfixturevalue("valid_data") + valid_data_copy = request.getfixturevalue("valid_data") + classifier.fit(valid_data) + assert valid_data == valid_data_copy + + @pytest.mark.parametrize( + ("invalid_data", "expected_error", "expected_error_msg"), + [ + ( + Table( + { + "id": [1, 4], + "feat1": ["a", 5], + "feat2": [3, 6], + "target": [0, 1], + }, + ).tag_columns(target_name="target", feature_names=["feat1", "feat2"]), + NonNumericColumnError, + ( + r"Tried to do a numerical operation on one or multiple non-numerical columns: \n\{'feat1'\}\nYou" + r" can use the LabelEncoder or OneHotEncoder to transform your non-numerical data to numerical" + r" data.\nThe OneHotEncoder should be used if you work with nominal data. If your data contains too" + r" many different values\nor is ordinal, you should use the LabelEncoder." + ), + ), + ( + Table( + { + "id": [1, 4], + "feat1": [None, 5], + "feat2": [3, 6], + "target": [0, 1], + }, + ).tag_columns(target_name="target", feature_names=["feat1", "feat2"]), + MissingValuesColumnError, + ( + r"Tried to do an operation on one or multiple columns containing missing values: \n\{'feat1'\}\nYou" + r" can use the Imputer to replace the missing values based on different strategies.\nIf you want to" + r" remove the missing values entirely you can use the method" + r" `Table.remove_rows_with_missing_values`." + ), + ), + ( + Table( + { + "id": [], + "feat1": [], + "feat2": [], + "target": [], + }, + ).tag_columns(target_name="target", feature_names=["feat1", "feat2"]), + DatasetMissesDataError, + r"Dataset contains no rows", + ), + ], + ids=["non-numerical data", "missing values in data", "no rows in data"], + ) + def test_should_raise_on_invalid_data( + self, + classifier: Classifier, + invalid_data: TaggedTable, + expected_error: Any, + expected_error_msg: str, + ) -> None: + with pytest.raises(expected_error, match=expected_error_msg): + classifier.fit(invalid_data) + + @pytest.mark.parametrize( + "table", + [ + Table( + { + "a": [1.0, 0.0, 0.0, 0.0], + "b": [0.0, 1.0, 1.0, 0.0], + "c": [0.0, 0.0, 0.0, 1.0], + }, + ), + ], + ids=["untagged_table"], + ) + def test_should_raise_if_table_is_not_tagged(self, classifier: Classifier, table: Table) -> None: + with pytest.raises(UntaggedTableError): + classifier.fit(table) # type: ignore[arg-type] + + +@pytest.mark.parametrize("classifier", classifiers(), ids=lambda x: x.__class__.__name__) +class TestPredict: + def test_should_include_features_of_input_table(self, classifier: Classifier, valid_data: TaggedTable) -> None: + fitted_classifier = classifier.fit(valid_data) + prediction = fitted_classifier.predict(valid_data.features) + assert prediction.features == valid_data.features + + def test_should_include_complete_input_table(self, classifier: Classifier, valid_data: TaggedTable) -> None: + fitted_regressor = classifier.fit(valid_data) + prediction = fitted_regressor.predict(valid_data.features) + assert prediction.features == valid_data.features + + def test_should_set_correct_target_name(self, classifier: Classifier, valid_data: TaggedTable) -> None: + fitted_classifier = classifier.fit(valid_data) + prediction = fitted_classifier.predict(valid_data.features) + assert prediction.target.name == "target" + + def test_should_not_change_input_table(self, classifier: Classifier, request: FixtureRequest) -> None: + valid_data = request.getfixturevalue("valid_data") + valid_data_copy = request.getfixturevalue("valid_data") + fitted_classifier = classifier.fit(valid_data) + fitted_classifier.predict(valid_data.features) + assert valid_data == valid_data_copy + + def test_should_raise_if_not_fitted(self, classifier: Classifier, valid_data: TaggedTable) -> None: + with pytest.raises(ModelNotFittedError): + classifier.predict(valid_data.features) + + def test_should_raise_if_dataset_contains_target(self, classifier: Classifier, valid_data: TaggedTable) -> None: + fitted_classifier = classifier.fit(valid_data) + with pytest.raises(DatasetContainsTargetError, match="target"): + fitted_classifier.predict(valid_data) + + def test_should_raise_if_dataset_misses_features(self, classifier: Classifier, valid_data: TaggedTable) -> None: + fitted_classifier = classifier.fit(valid_data) + with pytest.raises(DatasetMissesFeaturesError, match="[feat1, feat2]"): + fitted_classifier.predict(valid_data.features.remove_columns(["feat1", "feat2"])) + + @pytest.mark.parametrize( + ("invalid_data", "expected_error", "expected_error_msg"), + [ + ( + Table( + { + "id": [1, 4], + "feat1": ["a", 5], + "feat2": [3, 6], + }, + ), + NonNumericColumnError, + r"Tried to do a numerical operation on one or multiple non-numerical columns: \n\{'feat1'\}", + ), + ( + Table( + { + "id": [1, 4], + "feat1": [None, 5], + "feat2": [3, 6], + }, + ), + MissingValuesColumnError, + r"Tried to do an operation on one or multiple columns containing missing values: \n\{'feat1'\}", + ), + ( + Table( + { + "id": [], + "feat1": [], + "feat2": [], + }, + ), + DatasetMissesDataError, + r"Dataset contains no rows", + ), + ], + ids=["non-numerical data", "missing values in data", "no rows in data"], + ) + def test_should_raise_on_invalid_data( + self, + classifier: Classifier, + valid_data: TaggedTable, + invalid_data: Table, + expected_error: Any, + expected_error_msg: str, + ) -> None: + classifier = classifier.fit(valid_data) + with pytest.raises(expected_error, match=expected_error_msg): + classifier.predict(invalid_data) + + +@pytest.mark.parametrize("classifier", classifiers(), ids=lambda x: x.__class__.__name__) +class TestIsFitted: + def test_should_return_false_before_fitting(self, classifier: Classifier) -> None: + assert not classifier.is_fitted() + + def test_should_return_true_after_fitting(self, classifier: Classifier, valid_data: TaggedTable) -> None: + fitted_classifier = classifier.fit(valid_data) + assert fitted_classifier.is_fitted() + + +class DummyClassifier(Classifier): + """ + Dummy classifier to test metrics. + + Metrics methods expect a `TaggedTable` as input with two columns: + + - `predicted`: The predicted targets. + - `expected`: The correct targets. + + `target_name` must be set to `"expected"`. + """ + + def fit(self, training_set: TaggedTable) -> DummyClassifier: # noqa: ARG002 + return self + + def predict(self, dataset: Table) -> TaggedTable: + # Needed until https://github.com/Safe-DS/Stdlib/issues/75 is fixed + predicted = dataset.get_column("predicted") + feature = predicted.rename("feature") + dataset = Table.from_columns([feature, predicted]) + + return dataset.tag_columns(target_name="predicted") + + def is_fitted(self) -> bool: + return True + + def _get_sklearn_classifier(self) -> ClassifierMixin: + pass + + +class TestAccuracy: + def test_with_same_type(self) -> None: + table = Table( + { + "predicted": [1, 2, 3, 4], + "expected": [1, 2, 3, 3], + }, + ).tag_columns(target_name="expected") + + assert DummyClassifier().accuracy(table) == 0.75 + + def test_with_different_types(self) -> None: + table = Table( + { + "predicted": ["1", "2", "3", "4"], + "expected": [1, 2, 3, 3], + }, + ).tag_columns(target_name="expected") + + assert DummyClassifier().accuracy(table) == 0.0 + + @pytest.mark.parametrize( + "table", + [ + Table( + { + "a": [1.0, 0.0, 0.0, 0.0], + "b": [0.0, 1.0, 1.0, 0.0], + "c": [0.0, 0.0, 0.0, 1.0], + }, + ), + ], + ids=["untagged_table"], + ) + def test_should_raise_if_table_is_not_tagged(self, table: Table) -> None: + with pytest.raises(UntaggedTableError): + DummyClassifier().accuracy(table) # type: ignore[arg-type] + + +class TestPrecision: + def test_should_compare_result(self) -> None: + table = Table( + { + "predicted": [1, 1, 0, 2], + "expected": [1, 0, 1, 2], + }, + ).tag_columns(target_name="expected") + + assert DummyClassifier().precision(table, 1) == 0.5 + + def test_should_compare_result_with_different_types(self) -> None: + table = Table( + { + "predicted": [1, "1", "0", "2"], + "expected": [1, 0, 1, 2], + }, + ).tag_columns(target_name="expected") + + assert DummyClassifier().precision(table, 1) == 1.0 + + def test_should_return_1_if_never_expected_to_be_positive(self) -> None: + table = Table( + { + "predicted": ["lol", "1", "0", "2"], + "expected": [1, 0, 1, 2], + }, + ).tag_columns(target_name="expected") + + assert DummyClassifier().precision(table, 1) == 1.0 + + @pytest.mark.parametrize( + "table", + [ + Table( + { + "a": [1.0, 0.0, 0.0, 0.0], + "b": [0.0, 1.0, 1.0, 0.0], + "c": [0.0, 0.0, 0.0, 1.0], + }, + ), + ], + ids=["untagged_table"], + ) + def test_should_raise_if_table_is_not_tagged(self, table: Table) -> None: + with pytest.raises(UntaggedTableError): + DummyClassifier().precision(table, 1) # type: ignore[arg-type] + + +class TestRecall: + def test_should_compare_result(self) -> None: + table = Table( + { + "predicted": [1, 1, 0, 2], + "expected": [1, 0, 1, 2], + }, + ).tag_columns(target_name="expected") + + assert DummyClassifier().recall(table, 1) == 0.5 + + def test_should_compare_result_with_different_types(self) -> None: + table = Table( + { + "predicted": [1, "1", "0", "2"], + "expected": [1, 0, 1, 2], + }, + ).tag_columns(target_name="expected") + + assert DummyClassifier().recall(table, 1) == 0.5 + + def test_should_return_1_if_never_expected_to_be_positive(self) -> None: + table = Table( + { + "predicted": ["lol", "1", "0", "2"], + "expected": [2, 0, 5, 2], + }, + ).tag_columns(target_name="expected") + + assert DummyClassifier().recall(table, 1) == 1.0 + + @pytest.mark.parametrize( + "table", + [ + Table( + { + "a": [1.0, 0.0, 0.0, 0.0], + "b": [0.0, 1.0, 1.0, 0.0], + "c": [0.0, 0.0, 0.0, 1.0], + }, + ), + ], + ids=["untagged_table"], + ) + def test_should_raise_if_table_is_not_tagged(self, table: Table) -> None: + with pytest.raises(UntaggedTableError): + DummyClassifier().recall(table, 1) # type: ignore[arg-type] + + +class TestF1Score: + def test_should_compare_result(self) -> None: + table = Table( + { + "predicted": [1, 1, 0, 2], + "expected": [1, 0, 1, 2], + }, + ).tag_columns(target_name="expected") + + assert DummyClassifier().f1_score(table, 1) == 0.5 + + def test_should_compare_result_with_different_types(self) -> None: + table = Table( + { + "predicted": [1, "1", "0", "2"], + "expected": [1, 0, 1, 2], + }, + ).tag_columns(target_name="expected") + + assert DummyClassifier().f1_score(table, 1) == pytest.approx(0.6666667) + + def test_should_return_1_if_never_expected_or_predicted_to_be_positive(self) -> None: + table = Table( + { + "predicted": ["lol", "1", "0", "2"], + "expected": [2, 0, 2, 2], + }, + ).tag_columns(target_name="expected") + + assert DummyClassifier().f1_score(table, 1) == 1.0 + + @pytest.mark.parametrize( + "table", + [ + Table( + { + "a": [1.0, 0.0, 0.0, 0.0], + "b": [0.0, 1.0, 1.0, 0.0], + "c": [0.0, 0.0, 0.0, 1.0], + }, + ), + ], + ids=["untagged_table"], + ) + def test_should_raise_if_table_is_not_tagged(self, table: Table) -> None: + with pytest.raises(UntaggedTableError): + DummyClassifier().f1_score(table, 1) # type: ignore[arg-type] diff --git a/tests/safeds/ml/classical/classification/test_gradient_boosting.py b/tests/safeds/ml/classical/classification/test_gradient_boosting.py new file mode 100644 index 000000000..e30564036 --- /dev/null +++ b/tests/safeds/ml/classical/classification/test_gradient_boosting.py @@ -0,0 +1,39 @@ +import pytest +from safeds.data.tabular.containers import Table, TaggedTable +from safeds.ml.classical.classification import GradientBoosting + + +@pytest.fixture() +def training_set() -> TaggedTable: + table = Table({"col1": [1, 2, 3, 4], "col2": [1, 2, 3, 4]}) + return table.tag_columns(target_name="col1", feature_names=["col2"]) + + +class TestNumberOfTrees: + def test_should_be_passed_to_fitted_model(self, training_set: TaggedTable) -> None: + fitted_model = GradientBoosting(number_of_trees=2).fit(training_set) + assert fitted_model.number_of_trees == 2 + + def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None: + fitted_model = GradientBoosting(number_of_trees=2).fit(training_set) + assert fitted_model._wrapped_classifier is not None + assert fitted_model._wrapped_classifier.n_estimators == 2 + + def test_should_raise_if_less_than_1(self) -> None: + with pytest.raises(ValueError, match="The parameter 'number_of_trees' has to be greater than 0."): + GradientBoosting(number_of_trees=0) + + +class TestLearningRate: + def test_should_be_passed_to_fitted_model(self, training_set: TaggedTable) -> None: + fitted_model = GradientBoosting(learning_rate=2).fit(training_set) + assert fitted_model.learning_rate == 2 + + def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None: + fitted_model = GradientBoosting(learning_rate=2).fit(training_set) + assert fitted_model._wrapped_classifier is not None + assert fitted_model._wrapped_classifier.learning_rate == 2 + + def test_should_raise_if_less_than_or_equal_to_0(self) -> None: + with pytest.raises(ValueError, match="The parameter 'learning_rate' has to be greater than 0."): + GradientBoosting(learning_rate=-1) diff --git a/tests/safeds/ml/classical/classification/test_k_nearest_neighbors.py b/tests/safeds/ml/classical/classification/test_k_nearest_neighbors.py new file mode 100644 index 000000000..982f80184 --- /dev/null +++ b/tests/safeds/ml/classical/classification/test_k_nearest_neighbors.py @@ -0,0 +1,28 @@ +import pytest +from safeds.data.tabular.containers import Table, TaggedTable +from safeds.ml.classical.classification import KNearestNeighbors + + +@pytest.fixture() +def training_set() -> TaggedTable: + table = Table({"col1": [1, 2, 3, 4], "col2": [1, 2, 3, 4]}) + return table.tag_columns(target_name="col1", feature_names=["col2"]) + + +class TestNumberOfNeighbors: + def test_should_be_passed_to_fitted_model(self, training_set: TaggedTable) -> None: + fitted_model = KNearestNeighbors(number_of_neighbors=2).fit(training_set) + assert fitted_model.number_of_neighbors == 2 + + def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None: + fitted_model = KNearestNeighbors(number_of_neighbors=2).fit(training_set) + assert fitted_model._wrapped_classifier is not None + assert fitted_model._wrapped_classifier.n_neighbors == 2 + + def test_should_raise_if_less_than_or_equal_to_0(self) -> None: + with pytest.raises(ValueError, match="The parameter 'number_of_neighbors' has to be greater than 0."): + KNearestNeighbors(number_of_neighbors=-1) + + def test_should_raise_if_greater_than_sample_size(self, training_set: TaggedTable) -> None: + with pytest.raises(ValueError, match="has to be less than or equal to the sample size"): + KNearestNeighbors(number_of_neighbors=5).fit(training_set) diff --git a/tests/safeds/ml/classical/classification/test_random_forest.py b/tests/safeds/ml/classical/classification/test_random_forest.py new file mode 100644 index 000000000..c6df609cc --- /dev/null +++ b/tests/safeds/ml/classical/classification/test_random_forest.py @@ -0,0 +1,24 @@ +import pytest +from safeds.data.tabular.containers import Table, TaggedTable +from safeds.ml.classical.classification import RandomForest + + +@pytest.fixture() +def training_set() -> TaggedTable: + table = Table({"col1": [1, 2, 3, 4], "col2": [1, 2, 3, 4]}) + return table.tag_columns(target_name="col1", feature_names=["col2"]) + + +class TestNumberOfTrees: + def test_should_be_passed_to_fitted_model(self, training_set: TaggedTable) -> None: + fitted_model = RandomForest(number_of_trees=2).fit(training_set) + assert fitted_model.number_of_trees == 2 + + def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None: + fitted_model = RandomForest(number_of_trees=2).fit(training_set) + assert fitted_model._wrapped_classifier is not None + assert fitted_model._wrapped_classifier.n_estimators == 2 + + def test_should_raise_if_less_than_or_equal_to_0(self) -> None: + with pytest.raises(ValueError, match="The parameter 'number_of_trees' has to be greater than 0."): + RandomForest(number_of_trees=-1) diff --git a/tests/safeds/ml/classical/classification/test_support_vector_machine.py b/tests/safeds/ml/classical/classification/test_support_vector_machine.py new file mode 100644 index 000000000..d54b3d895 --- /dev/null +++ b/tests/safeds/ml/classical/classification/test_support_vector_machine.py @@ -0,0 +1,86 @@ +import pytest +from safeds.data.tabular.containers import Table, TaggedTable +from safeds.ml.classical.classification import SupportVectorMachine + + +@pytest.fixture() +def training_set() -> TaggedTable: + table = Table({"col1": [1, 2, 3, 4], "col2": [1, 2, 3, 4]}) + return table.tag_columns(target_name="col1", feature_names=["col2"]) + + +class TestC: + def test_should_be_passed_to_fitted_model(self, training_set: TaggedTable) -> None: + fitted_model = SupportVectorMachine(c=2).fit(training_set=training_set) + assert fitted_model.c == 2 + + def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None: + fitted_model = SupportVectorMachine(c=2).fit(training_set) + assert fitted_model._wrapped_classifier is not None + assert fitted_model._wrapped_classifier.C == 2 + + def test_should_raise_if_less_than_or_equal_to_0(self) -> None: + with pytest.raises( + ValueError, + match="The parameter 'c' has to be strictly positive.", + ): + SupportVectorMachine(c=-1) + + +class TestKernel: + def test_should_be_passed_to_fitted_model(self, training_set: TaggedTable) -> None: + kernel = SupportVectorMachine.Kernel.Linear() + fitted_model = SupportVectorMachine(c=2, kernel=kernel).fit(training_set=training_set) + assert isinstance(fitted_model.kernel, SupportVectorMachine.Kernel.Linear) + + def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None: + kernel = SupportVectorMachine.Kernel.Linear() + fitted_model = SupportVectorMachine(c=2, kernel=kernel).fit(training_set) + assert fitted_model._wrapped_classifier is not None + assert isinstance(fitted_model.kernel, SupportVectorMachine.Kernel.Linear) + + def test_should_get_sklearn_kernel_linear(self) -> None: + svm = SupportVectorMachine(c=2, kernel=SupportVectorMachine.Kernel.Linear()) + assert isinstance(svm.kernel, SupportVectorMachine.Kernel.Linear) + linear_kernel = svm.kernel.get_sklearn_kernel() + assert linear_kernel == "linear" + + def test_should_raise_if_degree_less_than_1(self) -> None: + with pytest.raises(ValueError, match="The parameter 'degree' has to be greater than or equal to 1."): + SupportVectorMachine.Kernel.Polynomial(degree=0) + + def test_should_get_sklearn_kernel_polynomial(self) -> None: + svm = SupportVectorMachine(c=2, kernel=SupportVectorMachine.Kernel.Polynomial(degree=2)) + assert isinstance(svm.kernel, SupportVectorMachine.Kernel.Polynomial) + poly_kernel = svm.kernel.get_sklearn_kernel() + assert poly_kernel == "poly" + + def test_should_get_sklearn_kernel_sigmoid(self) -> None: + svm = SupportVectorMachine(c=2, kernel=SupportVectorMachine.Kernel.Sigmoid()) + assert isinstance(svm.kernel, SupportVectorMachine.Kernel.Sigmoid) + sigmoid_kernel = svm.kernel.get_sklearn_kernel() + assert sigmoid_kernel == "sigmoid" + + def test_should_get_sklearn_kernel_rbf(self) -> None: + svm = SupportVectorMachine(c=2, kernel=SupportVectorMachine.Kernel.RadialBasisFunction()) + assert isinstance(svm.kernel, SupportVectorMachine.Kernel.RadialBasisFunction) + rbf_kernel = svm.kernel.get_sklearn_kernel() + assert rbf_kernel == "rbf" + + def test_should_get_kernel_name(self) -> None: + svm = SupportVectorMachine(c=2, kernel=SupportVectorMachine.Kernel.Linear()) + assert svm._get_kernel_name() == "linear" + + svm = SupportVectorMachine(c=2, kernel=SupportVectorMachine.Kernel.Polynomial(degree=2)) + assert svm._get_kernel_name() == "poly" + + svm = SupportVectorMachine(c=2, kernel=SupportVectorMachine.Kernel.Sigmoid()) + assert svm._get_kernel_name() == "sigmoid" + + svm = SupportVectorMachine(c=2, kernel=SupportVectorMachine.Kernel.RadialBasisFunction()) + assert svm._get_kernel_name() == "rbf" + + def test_should_get_kernel_name_invalid_kernel_type(self) -> None: + svm = SupportVectorMachine(c=2) + with pytest.raises(TypeError, match="Invalid kernel type."): + svm._get_kernel_name() diff --git a/tests/safeds/ml/classical/regression/__init__.py b/tests/safeds/ml/classical/regression/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/safeds/ml/classical/regression/test_ada_boost.py b/tests/safeds/ml/classical/regression/test_ada_boost.py new file mode 100644 index 000000000..73f3511bb --- /dev/null +++ b/tests/safeds/ml/classical/regression/test_ada_boost.py @@ -0,0 +1,52 @@ +import pytest +from safeds.data.tabular.containers import Table, TaggedTable +from safeds.ml.classical.regression import AdaBoost + + +@pytest.fixture() +def training_set() -> TaggedTable: + table = Table({"col1": [1, 2, 3, 4], "col2": [1, 2, 3, 4]}) + return table.tag_columns(target_name="col1", feature_names=["col2"]) + + +class TestLearner: + def test_should_be_passed_to_fitted_model(self, training_set: TaggedTable) -> None: + learner = AdaBoost() + fitted_model = AdaBoost(learner=learner).fit(training_set) + assert fitted_model.learner == learner + + def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None: + learner = AdaBoost() + fitted_model = AdaBoost(learner=learner).fit(training_set) + assert fitted_model._wrapped_regressor is not None + assert isinstance(fitted_model._wrapped_regressor.estimator, type(learner._get_sklearn_regressor())) + + +class TestMaximumNumberOfLearners: + def test_should_be_passed_to_fitted_model(self, training_set: TaggedTable) -> None: + fitted_model = AdaBoost(maximum_number_of_learners=2).fit(training_set) + assert fitted_model.maximum_number_of_learners == 2 + + def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None: + fitted_model = AdaBoost(maximum_number_of_learners=2).fit(training_set) + assert fitted_model._wrapped_regressor is not None + assert fitted_model._wrapped_regressor.n_estimators == 2 + + def test_should_raise_if_less_than_or_equal_to_0(self) -> None: + with pytest.raises(ValueError, match="The parameter 'maximum_number_of_learners' has to be grater than 0."): + AdaBoost(maximum_number_of_learners=-1) + + +class TestLearningRate: + def test_should_be_passed_to_fitted_model(self, training_set: TaggedTable) -> None: + fitted_model = AdaBoost(learning_rate=2).fit(training_set) + assert fitted_model.learning_rate == 2 + + def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None: + fitted_model = AdaBoost(learning_rate=2).fit(training_set) + assert fitted_model._wrapped_regressor is not None + assert fitted_model._wrapped_regressor.learning_rate == 2 + + def test_should_raise_if_less_than_or_equal_to_0(self) -> None: + with pytest.raises(ValueError, match="The parameter 'learning_rate' has to be greater than 0."): + AdaBoost(learning_rate=-1) diff --git a/tests/safeds/ml/classical/regression/test_elastic_net_regression.py b/tests/safeds/ml/classical/regression/test_elastic_net_regression.py new file mode 100644 index 000000000..f8e47ae61 --- /dev/null +++ b/tests/safeds/ml/classical/regression/test_elastic_net_regression.py @@ -0,0 +1,73 @@ +import pytest +from safeds.data.tabular.containers import Table, TaggedTable +from safeds.ml.classical.regression import ElasticNetRegression + + +@pytest.fixture() +def training_set() -> TaggedTable: + table = Table({"col1": [1, 2, 3, 4], "col2": [1, 2, 3, 4]}) + return table.tag_columns(target_name="col1", feature_names=["col2"]) + + +class TestAlpha: + def test_should_be_passed_to_fitted_model(self, training_set: TaggedTable) -> None: + fitted_model = ElasticNetRegression(alpha=1).fit(training_set) + assert fitted_model.alpha == 1 + + def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None: + fitted_model = ElasticNetRegression(alpha=1).fit(training_set) + assert fitted_model._wrapped_regressor is not None + assert fitted_model._wrapped_regressor.alpha == 1 + + def test_should_raise_if_less_than_0(self) -> None: + with pytest.raises(ValueError, match="The parameter 'alpha' must be non-negative"): + ElasticNetRegression(alpha=-1) + + def test_should_warn_if_equal_to_0(self) -> None: + with pytest.warns( + UserWarning, + match=( + "Setting alpha to zero makes this model equivalent to LinearRegression. You " + "should use LinearRegression instead for better numerical stability." + ), + ): + ElasticNetRegression(alpha=0) + + +class TestLassoRatio: + def test_should_be_passed_to_fitted_model(self, training_set: TaggedTable) -> None: + fitted_model = ElasticNetRegression(lasso_ratio=0.3).fit(training_set) + assert fitted_model.lasso_ratio == 0.3 + + def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None: + fitted_model = ElasticNetRegression(lasso_ratio=0.3).fit(training_set) + assert fitted_model._wrapped_regressor is not None + assert fitted_model._wrapped_regressor.l1_ratio == 0.3 + + def test_should_raise_if_less_than_0(self) -> None: + with pytest.raises(ValueError, match="The parameter 'lasso_ratio' must be between 0 and 1."): + ElasticNetRegression(lasso_ratio=-1.0) + + def test_should_raise_if_greater_than_1(self) -> None: + with pytest.raises(ValueError, match="The parameter 'lasso_ratio' must be between 0 and 1."): + ElasticNetRegression(lasso_ratio=2.0) + + def test_should_warn_if_0(self) -> None: + with pytest.warns( + UserWarning, + match=( + "ElasticNetRegression with lasso_ratio = 0 is essentially RidgeRegression." + " Use RidgeRegression instead for better numerical stability." + ), + ): + ElasticNetRegression(lasso_ratio=0) + + def test_should_warn_if_1(self) -> None: + with pytest.warns( + UserWarning, + match=( + "ElasticNetRegression with lasso_ratio = 0 is essentially LassoRegression." + " Use LassoRegression instead for better numerical stability." + ), + ): + ElasticNetRegression(lasso_ratio=1) diff --git a/tests/safeds/ml/classical/regression/test_gradient_boosting.py b/tests/safeds/ml/classical/regression/test_gradient_boosting.py new file mode 100644 index 000000000..bf92e381e --- /dev/null +++ b/tests/safeds/ml/classical/regression/test_gradient_boosting.py @@ -0,0 +1,39 @@ +import pytest +from safeds.data.tabular.containers import Table, TaggedTable +from safeds.ml.classical.regression import GradientBoosting + + +@pytest.fixture() +def training_set() -> TaggedTable: + table = Table({"col1": [1, 2, 3, 4], "col2": [1, 2, 3, 4]}) + return table.tag_columns(target_name="col1", feature_names=["col2"]) + + +class TestNumberOfTrees: + def test_should_be_passed_to_fitted_model(self, training_set: TaggedTable) -> None: + fitted_model = GradientBoosting(number_of_trees=2).fit(training_set) + assert fitted_model.number_of_trees == 2 + + def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None: + fitted_model = GradientBoosting(number_of_trees=2).fit(training_set) + assert fitted_model._wrapped_regressor is not None + assert fitted_model._wrapped_regressor.n_estimators == 2 + + def test_should_raise_if_less_than_1(self) -> None: + with pytest.raises(ValueError, match="The parameter 'number_of_trees' has to be greater than 0."): + GradientBoosting(number_of_trees=0) + + +class TestLearningRate: + def test_should_be_passed_to_fitted_model(self, training_set: TaggedTable) -> None: + fitted_model = GradientBoosting(learning_rate=2).fit(training_set) + assert fitted_model.learning_rate == 2 + + def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None: + fitted_model = GradientBoosting(learning_rate=2).fit(training_set) + assert fitted_model._wrapped_regressor is not None + assert fitted_model._wrapped_regressor.learning_rate == 2 + + def test_should_raise_if_less_than_or_equal_to_0(self) -> None: + with pytest.raises(ValueError, match="The parameter 'learning_rate' has to be greater than 0."): + GradientBoosting(learning_rate=-1) diff --git a/tests/safeds/ml/classical/regression/test_k_nearest_neighbors.py b/tests/safeds/ml/classical/regression/test_k_nearest_neighbors.py new file mode 100644 index 000000000..5450a99f3 --- /dev/null +++ b/tests/safeds/ml/classical/regression/test_k_nearest_neighbors.py @@ -0,0 +1,28 @@ +import pytest +from safeds.data.tabular.containers import Table, TaggedTable +from safeds.ml.classical.regression import KNearestNeighbors + + +@pytest.fixture() +def training_set() -> TaggedTable: + table = Table({"col1": [1, 2, 3, 4], "col2": [1, 2, 3, 4]}) + return table.tag_columns(target_name="col1", feature_names=["col2"]) + + +class TestNumberOfNeighbors: + def test_should_be_passed_to_fitted_model(self, training_set: TaggedTable) -> None: + fitted_model = KNearestNeighbors(number_of_neighbors=2).fit(training_set) + assert fitted_model.number_of_neighbors == 2 + + def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None: + fitted_model = KNearestNeighbors(number_of_neighbors=2).fit(training_set) + assert fitted_model._wrapped_regressor is not None + assert fitted_model._wrapped_regressor.n_neighbors == 2 + + def test_should_raise_if_less_than_or_equal_to_0(self) -> None: + with pytest.raises(ValueError, match="The parameter 'number_of_neighbors' has to be greater than 0."): + KNearestNeighbors(number_of_neighbors=-1) + + def test_should_raise_if_greater_than_sample_size(self, training_set: TaggedTable) -> None: + with pytest.raises(ValueError, match="has to be less than or equal to the sample size"): + KNearestNeighbors(number_of_neighbors=5).fit(training_set) diff --git a/tests/safeds/ml/classical/regression/test_lasso_regression.py b/tests/safeds/ml/classical/regression/test_lasso_regression.py new file mode 100644 index 000000000..332b9f1ad --- /dev/null +++ b/tests/safeds/ml/classical/regression/test_lasso_regression.py @@ -0,0 +1,34 @@ +import pytest +from safeds.data.tabular.containers import Table, TaggedTable +from safeds.ml.classical.regression import LassoRegression + + +@pytest.fixture() +def training_set() -> TaggedTable: + table = Table({"col1": [1, 2, 3, 4], "col2": [1, 2, 3, 4]}) + return table.tag_columns(target_name="col1", feature_names=["col2"]) + + +class TestAlpha: + def test_should_be_passed_to_fitted_model(self, training_set: TaggedTable) -> None: + fitted_model = LassoRegression(alpha=1).fit(training_set) + assert fitted_model.alpha == 1 + + def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None: + fitted_model = LassoRegression(alpha=1).fit(training_set) + assert fitted_model._wrapped_regressor is not None + assert fitted_model._wrapped_regressor.alpha == 1 + + def test_should_raise_if_less_than_0(self) -> None: + with pytest.raises(ValueError, match="The parameter 'alpha' must be non-negative"): + LassoRegression(alpha=-1) + + def test_should_warn_if_equal_to_0(self) -> None: + with pytest.warns( + UserWarning, + match=( + "Setting alpha to zero makes this model equivalent to LinearRegression. You " + "should use LinearRegression instead for better numerical stability." + ), + ): + LassoRegression(alpha=0) diff --git a/tests/safeds/ml/classical/regression/test_random_forest.py b/tests/safeds/ml/classical/regression/test_random_forest.py new file mode 100644 index 000000000..89f103704 --- /dev/null +++ b/tests/safeds/ml/classical/regression/test_random_forest.py @@ -0,0 +1,24 @@ +import pytest +from safeds.data.tabular.containers import Table, TaggedTable +from safeds.ml.classical.regression import RandomForest + + +@pytest.fixture() +def training_set() -> TaggedTable: + table = Table({"col1": [1, 2, 3, 4], "col2": [1, 2, 3, 4]}) + return table.tag_columns(target_name="col1", feature_names=["col2"]) + + +class TestNumberOfTrees: + def test_should_be_passed_to_fitted_model(self, training_set: TaggedTable) -> None: + fitted_model = RandomForest(number_of_trees=2).fit(training_set) + assert fitted_model.number_of_trees == 2 + + def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None: + fitted_model = RandomForest(number_of_trees=2).fit(training_set) + assert fitted_model._wrapped_regressor is not None + assert fitted_model._wrapped_regressor.n_estimators == 2 + + def test_should_raise_if_less_than_or_equal_to_0(self) -> None: + with pytest.raises(ValueError, match="The parameter 'number_of_trees' has to be greater than 0."): + RandomForest(number_of_trees=-1) diff --git a/tests/safeds/ml/regression/test_regressor.py b/tests/safeds/ml/classical/regression/test_regressor.py similarity index 52% rename from tests/safeds/ml/regression/test_regressor.py rename to tests/safeds/ml/classical/regression/test_regressor.py index 500340815..0191ffb32 100644 --- a/tests/safeds/ml/regression/test_regressor.py +++ b/tests/safeds/ml/classical/regression/test_regressor.py @@ -1,19 +1,21 @@ from __future__ import annotations -from typing import TYPE_CHECKING +from typing import TYPE_CHECKING, Any import pandas as pd import pytest from safeds.data.tabular.containers import Column, Table, TaggedTable -from safeds.data.tabular.exceptions import ColumnLengthMismatchError -from safeds.ml.exceptions import ( +from safeds.exceptions import ( + ColumnLengthMismatchError, DatasetContainsTargetError, + DatasetMissesDataError, DatasetMissesFeaturesError, - LearningError, + MissingValuesColumnError, ModelNotFittedError, - PredictionError, + NonNumericColumnError, + UntaggedTableError, ) -from safeds.ml.regression import ( +from safeds.ml.classical.regression import ( AdaBoost, DecisionTree, ElasticNetRegression, @@ -24,13 +26,15 @@ RandomForest, Regressor, RidgeRegression, + SupportVectorMachine, ) # noinspection PyProtectedMember -from safeds.ml.regression._regressor import _check_metrics_preconditions +from safeds.ml.classical.regression._regressor import _check_metrics_preconditions if TYPE_CHECKING: from _pytest.fixtures import FixtureRequest + from sklearn.base import RegressorMixin def regressors() -> list[Regressor]: @@ -55,12 +59,13 @@ def regressors() -> list[Regressor]: LinearRegression(), RandomForest(), RidgeRegression(), + SupportVectorMachine(), ] @pytest.fixture() def valid_data() -> TaggedTable: - return Table.from_dict( + return Table( { "id": [1, 4], "feat1": [2, 5], @@ -70,18 +75,6 @@ def valid_data() -> TaggedTable: ).tag_columns(target_name="target", feature_names=["feat1", "feat2"]) -@pytest.fixture() -def invalid_data() -> TaggedTable: - return Table.from_dict( - { - "id": [1, 4], - "feat1": ["a", 5], - "feat2": [3, 6], - "target": [0, 1], - }, - ).tag_columns(target_name="target", feature_names=["feat1", "feat2"]) - - @pytest.mark.parametrize("regressor", regressors(), ids=lambda x: x.__class__.__name__) class TestFit: def test_should_succeed_on_valid_data(self, regressor: Regressor, valid_data: TaggedTable) -> None: @@ -98,10 +91,75 @@ def test_should_not_change_input_table(self, regressor: Regressor, request: Fixt regressor.fit(valid_data) assert valid_data == valid_data_copy - def test_should_raise_on_invalid_data(self, regressor: Regressor, invalid_data: TaggedTable) -> None: - with pytest.raises(LearningError): + @pytest.mark.parametrize( + ("invalid_data", "expected_error", "expected_error_msg"), + [ + ( + Table( + { + "id": [1, 4], + "feat1": ["a", 5], + "feat2": [3, 6], + "target": [0, 1], + }, + ).tag_columns(target_name="target", feature_names=["feat1", "feat2"]), + NonNumericColumnError, + r"Tried to do a numerical operation on one or multiple non-numerical columns: \n\{'feat1'\}", + ), + ( + Table( + { + "id": [1, 4], + "feat1": [None, 5], + "feat2": [3, 6], + "target": [0, 1], + }, + ).tag_columns(target_name="target", feature_names=["feat1", "feat2"]), + MissingValuesColumnError, + r"Tried to do an operation on one or multiple columns containing missing values: \n\{'feat1'\}", + ), + ( + Table( + { + "id": [], + "feat1": [], + "feat2": [], + "target": [], + }, + ).tag_columns(target_name="target", feature_names=["feat1", "feat2"]), + DatasetMissesDataError, + r"Dataset contains no rows", + ), + ], + ids=["non-numerical data", "missing values in data", "no rows in data"], + ) + def test_should_raise_on_invalid_data( + self, + regressor: Regressor, + invalid_data: TaggedTable, + expected_error: Any, + expected_error_msg: str, + ) -> None: + with pytest.raises(expected_error, match=expected_error_msg): regressor.fit(invalid_data) + @pytest.mark.parametrize( + "table", + [ + Table( + { + "a": [1.0, 0.0, 0.0, 0.0], + "b": [0.0, 1.0, 1.0, 0.0], + "c": [0.0, 0.0, 0.0, 1.0], + }, + ), + ], + ids=["untagged_table"], + ) + def test_should_raise_if_table_is_not_tagged(self, regressor: Regressor, table: Table) -> None: + with pytest.raises(UntaggedTableError): + regressor.fit(table) # type: ignore[arg-type] + @pytest.mark.parametrize("regressor", regressors(), ids=lambda x: x.__class__.__name__) class TestPredict: @@ -112,8 +170,8 @@ def test_should_include_features_of_input_table(self, regressor: Regressor, vali def test_should_include_complete_input_table(self, regressor: Regressor, valid_data: TaggedTable) -> None: fitted_regressor = regressor.fit(valid_data) - prediction = fitted_regressor.predict(valid_data.remove_columns(["target"])) - assert prediction.remove_columns(["target"]) == valid_data.remove_columns(["target"]) + prediction = fitted_regressor.predict(valid_data.features) + assert prediction.features == valid_data.features def test_should_set_correct_target_name(self, regressor: Regressor, valid_data: TaggedTable) -> None: fitted_regressor = regressor.fit(valid_data) @@ -139,17 +197,68 @@ def test_should_raise_if_dataset_contains_target(self, regressor: Regressor, val def test_should_raise_if_dataset_misses_features(self, regressor: Regressor, valid_data: TaggedTable) -> None: fitted_regressor = regressor.fit(valid_data) with pytest.raises(DatasetMissesFeaturesError, match="[feat1, feat2]"): - fitted_regressor.predict(valid_data.remove_columns(["feat1", "feat2", "target"])) + fitted_regressor.predict(valid_data.features.remove_columns(["feat1", "feat2"])) + @pytest.mark.parametrize( + ("invalid_data", "expected_error", "expected_error_msg"), + [ + ( + Table( + { + "id": [1, 4], + "feat1": ["a", 5], + "feat2": [3, 6], + }, + ), + NonNumericColumnError, + ( + r"Tried to do a numerical operation on one or multiple non-numerical columns: \n\{'feat1'\}\nYou" + r" can use the LabelEncoder or OneHotEncoder to transform your non-numerical data to numerical" + r" data.\nThe OneHotEncoder should be used if you work with nominal data. If your data contains too" + r" many different values\nor is ordinal, you should use the LabelEncoder." + ), + ), + ( + Table( + { + "id": [1, 4], + "feat1": [None, 5], + "feat2": [3, 6], + }, + ), + MissingValuesColumnError, + ( + r"Tried to do an operation on one or multiple columns containing missing values: \n\{'feat1'\}\nYou" + r" can use the Imputer to replace the missing values based on different strategies.\nIf you want to" + r" remove the missing values entirely you can use the method" + r" `Table.remove_rows_with_missing_values`." + ), + ), + ( + Table( + { + "id": [], + "feat1": [], + "feat2": [], + }, + ), + DatasetMissesDataError, + r"Dataset contains no rows", + ), + ], + ids=["non-numerical data", "missing values in data", "no rows in data"], + ) def test_should_raise_on_invalid_data( self, regressor: Regressor, valid_data: TaggedTable, - invalid_data: TaggedTable, + invalid_data: Table, + expected_error: Any, + expected_error_msg: str, ) -> None: - fitted_regressor = regressor.fit(valid_data) - with pytest.raises(PredictionError): - fitted_regressor.predict(invalid_data.features) + regressor = regressor.fit(valid_data) + with pytest.raises(expected_error, match=expected_error_msg): + regressor.predict(invalid_data) @pytest.mark.parametrize("regressor", regressors(), ids=lambda x: x.__class__.__name__) @@ -188,6 +297,9 @@ def predict(self, dataset: Table) -> TaggedTable: def is_fitted(self) -> bool: return True + def _get_sklearn_regressor(self) -> RegressorMixin: + pass + class TestMeanAbsoluteError: @pytest.mark.parametrize( @@ -201,7 +313,7 @@ class TestMeanAbsoluteError: ], ) def test_valid_data(self, predicted: list[float], expected: list[float], result: float) -> None: - table = Table.from_dict( + table = Table( { "predicted": predicted, "expected": expected, @@ -212,19 +324,54 @@ def test_valid_data(self, predicted: list[float], expected: list[float], result: assert DummyRegressor().mean_absolute_error(table) == result + @pytest.mark.parametrize( + "table", + [ + Table( + { + "a": [1.0, 0.0, 0.0, 0.0], + "b": [0.0, 1.0, 1.0, 0.0], + "c": [0.0, 0.0, 0.0, 1.0], + }, + ), + ], + ids=["untagged_table"], + ) + def test_should_raise_if_table_is_not_tagged(self, table: Table) -> None: + with pytest.raises(UntaggedTableError): + DummyRegressor().mean_absolute_error(table) # type: ignore[arg-type] + class TestMeanSquaredError: @pytest.mark.parametrize( ("predicted", "expected", "result"), [([1, 2], [1, 2], 0), ([0, 0], [1, 1], 1), ([1, 1, 1], [2, 2, 11], 34)], + ids=["perfect_prediction", "bad_prediction", "worst_prediction"], ) def test_valid_data(self, predicted: list[float], expected: list[float], result: float) -> None: - table = Table.from_dict({"predicted": predicted, "expected": expected}).tag_columns( + table = Table({"predicted": predicted, "expected": expected}).tag_columns( target_name="expected", ) assert DummyRegressor().mean_squared_error(table) == result + @pytest.mark.parametrize( + "table", + [ + Table( + { + "a": [1.0, 0.0, 0.0, 0.0], + "b": [0.0, 1.0, 1.0, 0.0], + "c": [0.0, 0.0, 0.0, 1.0], + }, + ), + ], + ids=["untagged_table"], + ) + def test_should_raise_if_table_is_not_tagged(self, table: Table) -> None: + with pytest.raises(UntaggedTableError): + DummyRegressor().mean_squared_error(table) # type: ignore[arg-type] + class TestCheckMetricsPreconditions: @pytest.mark.parametrize( @@ -241,7 +388,7 @@ def test_should_raise_if_validation_fails( expected: list[str | int], error: type[Exception], ) -> None: - actual_column = Column("actual", pd.Series(actual)) - expected_column = Column("expected", pd.Series(expected)) + actual_column: Column = Column("actual", pd.Series(actual)) + expected_column: Column = Column("expected", pd.Series(expected)) with pytest.raises(error): _check_metrics_preconditions(actual_column, expected_column) diff --git a/tests/safeds/ml/classical/regression/test_ridge_regression.py b/tests/safeds/ml/classical/regression/test_ridge_regression.py new file mode 100644 index 000000000..ee3feb0b8 --- /dev/null +++ b/tests/safeds/ml/classical/regression/test_ridge_regression.py @@ -0,0 +1,34 @@ +import pytest +from safeds.data.tabular.containers import Table, TaggedTable +from safeds.ml.classical.regression import RidgeRegression + + +@pytest.fixture() +def training_set() -> TaggedTable: + table = Table({"col1": [1, 2, 3, 4], "col2": [1, 2, 3, 4]}) + return table.tag_columns(target_name="col1", feature_names=["col2"]) + + +class TestAlpha: + def test_should_be_passed_to_fitted_model(self, training_set: TaggedTable) -> None: + fitted_model = RidgeRegression(alpha=1).fit(training_set) + assert fitted_model.alpha == 1 + + def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None: + fitted_model = RidgeRegression(alpha=1).fit(training_set) + assert fitted_model._wrapped_regressor is not None + assert fitted_model._wrapped_regressor.alpha == 1 + + def test_should_raise_if_less_than_0(self) -> None: + with pytest.raises(ValueError, match="The parameter 'alpha' must be non-negative"): + RidgeRegression(alpha=-1) + + def test_should_warn_if_equal_to_0(self) -> None: + with pytest.warns( + UserWarning, + match=( + "Setting alpha to zero makes this model equivalent to LinearRegression. You " + "should use LinearRegression instead for better numerical stability." + ), + ): + RidgeRegression(alpha=0) diff --git a/tests/safeds/ml/classical/regression/test_support_vector_machine.py b/tests/safeds/ml/classical/regression/test_support_vector_machine.py new file mode 100644 index 000000000..634c45901 --- /dev/null +++ b/tests/safeds/ml/classical/regression/test_support_vector_machine.py @@ -0,0 +1,86 @@ +import pytest +from safeds.data.tabular.containers import Table, TaggedTable +from safeds.ml.classical.regression import SupportVectorMachine + + +@pytest.fixture() +def training_set() -> TaggedTable: + table = Table({"col1": [1, 2, 3, 4], "col2": [1, 2, 3, 4]}) + return table.tag_columns(target_name="col1", feature_names=["col2"]) + + +class TestC: + def test_should_be_passed_to_fitted_model(self, training_set: TaggedTable) -> None: + fitted_model = SupportVectorMachine(c=2).fit(training_set=training_set) + assert fitted_model.c == 2 + + def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None: + fitted_model = SupportVectorMachine(c=2).fit(training_set) + assert fitted_model._wrapped_regressor is not None + assert fitted_model._wrapped_regressor.C == 2 + + def test_should_raise_if_less_than_or_equal_to_0(self) -> None: + with pytest.raises( + ValueError, + match="The parameter 'c' has to be strictly positive.", + ): + SupportVectorMachine(c=-1) + + +class TestKernel: + def test_should_be_passed_to_fitted_model(self, training_set: TaggedTable) -> None: + kernel = SupportVectorMachine.Kernel.Linear() + fitted_model = SupportVectorMachine(c=2, kernel=kernel).fit(training_set=training_set) + assert isinstance(fitted_model.kernel, SupportVectorMachine.Kernel.Linear) + + def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None: + kernel = SupportVectorMachine.Kernel.Linear() + fitted_model = SupportVectorMachine(c=2, kernel=kernel).fit(training_set) + assert fitted_model._wrapped_regressor is not None + assert isinstance(fitted_model.kernel, SupportVectorMachine.Kernel.Linear) + + def test_should_get_sklearn_kernel_linear(self) -> None: + svm = SupportVectorMachine(c=2, kernel=SupportVectorMachine.Kernel.Linear()) + assert isinstance(svm.kernel, SupportVectorMachine.Kernel.Linear) + linear_kernel = svm.kernel.get_sklearn_kernel() + assert linear_kernel == "linear" + + def test_should_raise_if_degree_less_than_1(self) -> None: + with pytest.raises(ValueError, match="The parameter 'degree' has to be greater than or equal to 1."): + SupportVectorMachine.Kernel.Polynomial(degree=0) + + def test_should_get_sklearn_kernel_polynomial(self) -> None: + svm = SupportVectorMachine(c=2, kernel=SupportVectorMachine.Kernel.Polynomial(degree=2)) + assert isinstance(svm.kernel, SupportVectorMachine.Kernel.Polynomial) + poly_kernel = svm.kernel.get_sklearn_kernel() + assert poly_kernel == "poly" + + def test_should_get_sklearn_kernel_sigmoid(self) -> None: + svm = SupportVectorMachine(c=2, kernel=SupportVectorMachine.Kernel.Sigmoid()) + assert isinstance(svm.kernel, SupportVectorMachine.Kernel.Sigmoid) + sigmoid_kernel = svm.kernel.get_sklearn_kernel() + assert sigmoid_kernel == "sigmoid" + + def test_should_get_sklearn_kernel_rbf(self) -> None: + svm = SupportVectorMachine(c=2, kernel=SupportVectorMachine.Kernel.RadialBasisFunction()) + assert isinstance(svm.kernel, SupportVectorMachine.Kernel.RadialBasisFunction) + rbf_kernel = svm.kernel.get_sklearn_kernel() + assert rbf_kernel == "rbf" + + def test_should_get_kernel_name(self) -> None: + svm = SupportVectorMachine(c=2, kernel=SupportVectorMachine.Kernel.Linear()) + assert svm._get_kernel_name() == "linear" + + svm = SupportVectorMachine(c=2, kernel=SupportVectorMachine.Kernel.Polynomial(degree=2)) + assert svm._get_kernel_name() == "poly" + + svm = SupportVectorMachine(c=2, kernel=SupportVectorMachine.Kernel.Sigmoid()) + assert svm._get_kernel_name() == "sigmoid" + + svm = SupportVectorMachine(c=2, kernel=SupportVectorMachine.Kernel.RadialBasisFunction()) + assert svm._get_kernel_name() == "rbf" + + def test_should_get_kernel_name_invalid_kernel_type(self) -> None: + svm = SupportVectorMachine(c=2) + with pytest.raises(TypeError, match="Invalid kernel type."): + svm._get_kernel_name() diff --git a/tests/safeds/ml/classical/test_util_sklearn.py b/tests/safeds/ml/classical/test_util_sklearn.py new file mode 100644 index 000000000..741b53d7f --- /dev/null +++ b/tests/safeds/ml/classical/test_util_sklearn.py @@ -0,0 +1,56 @@ +import warnings +from typing import Any + +import pytest +from safeds.data.tabular.containers import Table +from safeds.exceptions import LearningError, PredictionError +from safeds.ml.classical._util_sklearn import fit, predict +from safeds.ml.classical.regression import LinearRegression + + +def test_predict_should_not_warn_about_feature_names() -> None: + """See https://github.com/Safe-DS/Stdlib/issues/51.""" + training_set = Table({"a": [1, 2, 3], "b": [2, 4, 6]}).tag_columns(target_name="b") + + model = LinearRegression() + fitted_model = model.fit(training_set) + + test_set = Table({"a": [4, 5, 6]}) + + # No warning should be emitted + with warnings.catch_warnings(): + warnings.filterwarnings("error", message="X has feature names") + fitted_model.predict(test_set) + + +class MLModelRaiseValueErrorOnFitAndPredict: + x, y = None, None + + def fit(self, x: Any, y: Any) -> None: + # The Linter does not want unnecessary parameters, so we just assign them to the class values + self.x = x + self.y = y + raise ValueError("Raise ValueError (LearningError) in fit for Test") + + def predict(self, x: Any) -> None: + # The Linter does not want unnecessary parameters, so we just assign it to the class value + self.x = x + raise ValueError("Raise ValueError (PredictionError) in predict for Test") + + +def test_should_raise_learning_error() -> None: + tagged_table = Table({"col1": [1, 2], "col2": [3, 4], "col3": [5, 6]}).tag_columns("col3") + with pytest.raises( + LearningError, + match=r"Error occurred while learning: Raise ValueError \(LearningError\) in fit for Test", + ): + fit(MLModelRaiseValueErrorOnFitAndPredict(), tagged_table) + + +def test_should_raise_prediction_error() -> None: + table = Table({"col1": [1, 2], "col2": [3, 4]}) + with pytest.raises( + PredictionError, + match=r"Error occurred while predicting: Raise ValueError \(PredictionError\) in predict for Test", + ): + predict(MLModelRaiseValueErrorOnFitAndPredict(), table, ["col1", "col2"], "col3") diff --git a/tests/safeds/ml/classification/test_classifier.py b/tests/safeds/ml/classification/test_classifier.py deleted file mode 100644 index 80f933943..000000000 --- a/tests/safeds/ml/classification/test_classifier.py +++ /dev/null @@ -1,193 +0,0 @@ -from __future__ import annotations - -from typing import TYPE_CHECKING - -import pytest -from safeds.data.tabular.containers import Table, TaggedTable -from safeds.ml.classification import ( - AdaBoost, - Classifier, - DecisionTree, - GradientBoosting, - KNearestNeighbors, - LogisticRegression, - RandomForest, -) -from safeds.ml.exceptions import ( - DatasetContainsTargetError, - DatasetMissesFeaturesError, - LearningError, - ModelNotFittedError, - PredictionError, -) - -if TYPE_CHECKING: - from _pytest.fixtures import FixtureRequest - - -def classifiers() -> list[Classifier]: - """ - Return the list of classifiers to test. - - After you implemented a new classifier, add it to this list to ensure its `fit` and `predict` method work as - expected. Place tests of methods that are specific to your classifier in a separate test file. - - Returns - ------- - classifiers : list[Classifier] - The list of classifiers to test. - """ - return [AdaBoost(), DecisionTree(), GradientBoosting(), KNearestNeighbors(2), LogisticRegression(), RandomForest()] - - -@pytest.fixture() -def valid_data() -> TaggedTable: - return Table.from_dict( - { - "id": [1, 4], - "feat1": [2, 5], - "feat2": [3, 6], - "target": [0, 1], - }, - ).tag_columns(target_name="target", feature_names=["feat1", "feat2"]) - - -@pytest.fixture() -def invalid_data() -> TaggedTable: - return Table.from_dict( - { - "id": [1, 4], - "feat1": ["a", 5], - "feat2": [3, 6], - "target": [0, 1], - }, - ).tag_columns(target_name="target", feature_names=["feat1", "feat2"]) - - -@pytest.mark.parametrize("classifier", classifiers(), ids=lambda x: x.__class__.__name__) -class TestFit: - def test_should_succeed_on_valid_data(self, classifier: Classifier, valid_data: TaggedTable) -> None: - classifier.fit(valid_data) - assert True # This asserts that the fit method succeeds - - def test_should_not_change_input_classifier(self, classifier: Classifier, valid_data: TaggedTable) -> None: - classifier.fit(valid_data) - assert not classifier.is_fitted() - - def test_should_not_change_input_table(self, classifier: Classifier, request: FixtureRequest) -> None: - valid_data = request.getfixturevalue("valid_data") - valid_data_copy = request.getfixturevalue("valid_data") - classifier.fit(valid_data) - assert valid_data == valid_data_copy - - def test_should_raise_on_invalid_data(self, classifier: Classifier, invalid_data: TaggedTable) -> None: - with pytest.raises(LearningError): - classifier.fit(invalid_data) - - -@pytest.mark.parametrize("classifier", classifiers(), ids=lambda x: x.__class__.__name__) -class TestPredict: - def test_should_include_features_of_input_table(self, classifier: Classifier, valid_data: TaggedTable) -> None: - fitted_classifier = classifier.fit(valid_data) - prediction = fitted_classifier.predict(valid_data.features) - assert prediction.features == valid_data.features - - def test_should_include_complete_input_table(self, classifier: Classifier, valid_data: TaggedTable) -> None: - fitted_regressor = classifier.fit(valid_data) - prediction = fitted_regressor.predict(valid_data.remove_columns(["target"])) - assert prediction.remove_columns(["target"]) == valid_data.remove_columns(["target"]) - - def test_should_set_correct_target_name(self, classifier: Classifier, valid_data: TaggedTable) -> None: - fitted_classifier = classifier.fit(valid_data) - prediction = fitted_classifier.predict(valid_data.features) - assert prediction.target.name == "target" - - def test_should_not_change_input_table(self, classifier: Classifier, request: FixtureRequest) -> None: - valid_data = request.getfixturevalue("valid_data") - valid_data_copy = request.getfixturevalue("valid_data") - fitted_classifier = classifier.fit(valid_data) - fitted_classifier.predict(valid_data.features) - assert valid_data == valid_data_copy - - def test_should_raise_if_not_fitted(self, classifier: Classifier, valid_data: TaggedTable) -> None: - with pytest.raises(ModelNotFittedError): - classifier.predict(valid_data.features) - - def test_should_raise_if_dataset_contains_target(self, classifier: Classifier, valid_data: TaggedTable) -> None: - fitted_classifier = classifier.fit(valid_data) - with pytest.raises(DatasetContainsTargetError, match="target"): - fitted_classifier.predict(valid_data) - - def test_should_raise_if_dataset_misses_features(self, classifier: Classifier, valid_data: TaggedTable) -> None: - fitted_classifier = classifier.fit(valid_data) - with pytest.raises(DatasetMissesFeaturesError, match="[feat1, feat2]"): - fitted_classifier.predict(valid_data.remove_columns(["feat1", "feat2", "target"])) - - def test_should_raise_on_invalid_data( - self, - classifier: Classifier, - valid_data: TaggedTable, - invalid_data: TaggedTable, - ) -> None: - fitted_classifier = classifier.fit(valid_data) - with pytest.raises(PredictionError): - fitted_classifier.predict(invalid_data.features) - - -@pytest.mark.parametrize("classifier", classifiers(), ids=lambda x: x.__class__.__name__) -class TestIsFitted: - def test_should_return_false_before_fitting(self, classifier: Classifier) -> None: - assert not classifier.is_fitted() - - def test_should_return_true_after_fitting(self, classifier: Classifier, valid_data: TaggedTable) -> None: - fitted_classifier = classifier.fit(valid_data) - assert fitted_classifier.is_fitted() - - -class DummyClassifier(Classifier): - """ - Dummy classifier to test metrics. - - Metrics methods expect a `TaggedTable` as input with two columns: - - - `predicted`: The predicted targets. - - `expected`: The correct targets. - - `target_name` must be set to `"expected"`. - """ - - def fit(self, training_set: TaggedTable) -> DummyClassifier: # noqa: ARG002 - return self - - def predict(self, dataset: Table) -> TaggedTable: - # Needed until https://github.com/Safe-DS/Stdlib/issues/75 is fixed - predicted = dataset.get_column("predicted") - feature = predicted.rename("feature") - dataset = Table.from_columns([feature, predicted]) - - return dataset.tag_columns(target_name="predicted") - - def is_fitted(self) -> bool: - return True - - -class TestAccuracy: - def test_with_same_type(self) -> None: - table = Table.from_dict( - { - "predicted": [1, 2, 3, 4], - "expected": [1, 2, 3, 3], - }, - ).tag_columns(target_name="expected") - - assert DummyClassifier().accuracy(table) == 0.75 - - def test_with_different_types(self) -> None: - table = Table.from_dict( - { - "predicted": ["1", "2", "3", "4"], - "expected": [1, 2, 3, 3], - }, - ).tag_columns(target_name="expected") - - assert DummyClassifier().accuracy(table) == 0.0 diff --git a/tests/safeds/ml/hyperparameters/__init__.py b/tests/safeds/ml/hyperparameters/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/safeds/ml/hyperparameters/test_choice.py b/tests/safeds/ml/hyperparameters/test_choice.py new file mode 100644 index 000000000..8adcd5952 --- /dev/null +++ b/tests/safeds/ml/hyperparameters/test_choice.py @@ -0,0 +1,63 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING + +import pytest +from safeds.ml.hyperparameters import Choice + +if TYPE_CHECKING: + from typing import Any + + +class TestContains: + @pytest.mark.parametrize( + ("choice", "value", "expected"), + [ + (Choice(1, 2, 3), 1, True), + (Choice(1, 2, 3), 2, True), + (Choice(1, 2, 3), 3, True), + (Choice(1, 2, 3), 4, False), + (Choice(1, 2, 3), "3", False), + ], + ids=[ + "value in choice (start)", + "value in choice (middle)", + "value in choice (end)", + "value not in choice", + "value not in choice (wrong type)", + ], + ) + def test_should_check_whether_choice_contains_value(self, choice: Choice, value: Any, expected: bool) -> None: + assert (value in choice) == expected + + +class TestIter: + @pytest.mark.parametrize( + ("choice", "expected"), + [ + (Choice(), []), + (Choice(1, 2, 3), [1, 2, 3]), + ], + ids=[ + "empty", + "non-empty", + ], + ) + def test_should_iterate_values(self, choice: Choice, expected: list[Any]) -> None: + assert list(choice) == expected + + +class TestLen: + @pytest.mark.parametrize( + ("choice", "expected"), + [ + (Choice(), 0), + (Choice(1, 2, 3), 3), + ], + ids=[ + "empty", + "non-empty", + ], + ) + def test_should_return_number_of_values(self, choice: Choice, expected: int) -> None: + assert len(choice) == expected diff --git a/tests/safeds/ml/test_util_sklearn.py b/tests/safeds/ml/test_util_sklearn.py deleted file mode 100644 index 4bcff1a88..000000000 --- a/tests/safeds/ml/test_util_sklearn.py +++ /dev/null @@ -1,19 +0,0 @@ -import warnings - -from safeds.data.tabular.containers import Table -from safeds.ml.regression import LinearRegression - - -def test_predict_should_not_warn_about_feature_names() -> None: - """See https://github.com/Safe-DS/Stdlib/issues/51.""" - training_set = Table({"a": [1, 2, 3], "b": [2, 4, 6]}).tag_columns(target_name="b") - - model = LinearRegression() - fitted_model = model.fit(training_set) - - test_set = Table({"a": [4, 5, 6]}) - - # No warning should be emitted - with warnings.catch_warnings(): - warnings.filterwarnings("error", message="X has feature names") - fitted_model.predict(test_set)